Dontopedia

GMWM

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-17.)

GMWM has 50 facts recorded in Dontopedia across 1 reference, with 11 live disagreements.

50 facts·25 predicates·1 sources·11 in dispute

Mostly:has auc score on task(10), tp:verdict reason(5), excludes class(4)

Maturity scale raw canonical shape-checked rule-derived certified

Full NamefullName

  • Google multispecies whale model[1]all time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Has Auc Score on Taskin disputehasAucScoreOnTask

  • 0.89[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.914[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.764[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.821[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.936[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.954[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.868[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.917[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.823[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • 0.855[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Inbound mentions (8)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

comparesPerformanceOfCompares Performance of(2)

hasDataContaminationHas Data Contamination(2)

outperformsOutperforms(1)

outperformsOnCetaceanTasksOutperforms on Cetacean Tasks(1)

partiallySeenForModelPartially Seen for Model(1)

transferPerformanceWorseThanTransfer Performance Worse Than(1)

Other facts (37)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

37 facts
PredicateValueRef
Tp:verdict ReasonThe dataset or training/evaluation relationship is grounded in the staged manuscript.[1]
Tp:verdict ReasonThe claimed model property matches the parsed model-property table in the staged manuscript.[1]
Tp:verdict ReasonThe reported AUC value is present in Table 2, but it was not recomputed from audio, labels, embeddings, and the few-shot sampling protocol.[1]
Tp:verdict ReasonThe claim is source-grounded, but the unit's executable recomputation requirement is blocked by missing experiment artifacts.[1]
Tp:verdict ReasonThe manuscript states the visualization interpretation, but no embeddings or figure-generation data are available for quantitative verification.[1]
Excludes ClassSei Whale[1]
Excludes ClassAnthropomorphic Noise[1]
Excludes ClassUnknown Whale[1]
Excludes ClassSei Whales[1]
Rdf:typeModel[1]
Rdf:typeEmbedding Model[1]
Has Sample Rate24000[1]
Has Sample Rate24[1]
Has Model Parameter Count4.1M[1]
Has Model Parameter Count4.1[1]
Poor Performance ReasonOverfitting to Microphone[1]
Poor Performance ReasonOverfitting to Training Characteristics[1]
Described inAllen Et Al 2024[1]
Described inHarvey Et Al 2024[1]
Published byAllen Et Al 2024[1]
Published byHarvey Et Al 2024[1]
Tp:simulation Verdictreproduced[1]
Tp:simulation Verdictinconclusive[1]
Has Window Size3[1]
Has Embedding Dimension1280[1]
Trained on TaxaWhales[1]
Has AliasGMWM[1]
Trained on DatasetNoaa Pipan[1]
Trained on ClassKiller Whale[1]
Has Performance0.612[1]
Evaluation ModeGmwm Off the Shelf[1]
Few Shot Performance0.954[1]
Transfer Performance Worse ThanGmwm[1]
Has Training TaxaWhales[1]
Trained onNoaa Pipan[1]
Has AbbreviationGMWM[1]
Shows Weak Linear Separabilitytrue[1]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:Model
labeltp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
Google multispecies whale model
hasSampleRatetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
24000
hasWindowSizetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
3
hasEmbeddingDimensiontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
1280
hasModelParameterCounttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
4.1M
trainedOnTaxatp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:whales
typetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:EmbeddingModel
hasAliastp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
GMWM
trainedOnDatasettp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:noaa-pipan
excludesClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:sei-whale
excludesClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:anthropomorphic-noise
excludesClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:unknown-whale
trainedOnClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:killer-whale
hasPerformancetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.612
evaluationModetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:gmwm-off-the-shelf
fewShotPerformancetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.954
poorPerformanceReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:overfitting-to-microphone
poorPerformanceReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:overfitting-to-training-characteristics
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.89
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.914
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.764
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.821
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.936
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.954
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.868
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.917
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.823
hasAucScoreOnTasktp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
0.855
transferPerformanceWorseThantp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:gmwm
labeltp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
GMWM
fullNametp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
Google multispecies whale model
hasSampleRatetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
24
hasModelParameterCounttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
4.1
hasTrainingTaxatp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:whales
describedIntp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:allen-et-al-2024
describedIntp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:harvey-et-al-2024
publishedBytp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:allen-et-al-2024
publishedBytp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:harvey-et-al-2024
trainedOntp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:noaa-pipan
excludesClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:sei-whales
hasAbbreviationtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
GMWM
showsWeakLinearSeparabilitytp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
true
simulationVerdicttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
reproduced
verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
The dataset or training/evaluation relationship is grounded in the staged manuscript.
verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
The claimed model property matches the parsed model-property table in the staged manuscript.
simulationVerdicttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
inconclusive
verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
The reported AUC value is present in Table 2, but it was not recomputed from audio, labels, embeddings, and the few-shot sampling protocol.
verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
The claim is source-grounded, but the unit's executable recomputation requirement is blocked by missing experiment artifacts.
verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
The manuscript states the visualization interpretation, but no embeddings or figure-generation data are available for quantitative verification.

References (1)

1 references
  1. tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
    • full textchunk-009
      text/plain3 KBdoc:agent/chunk-009/f33235ee-7e4c-40ec-b809-de198012fc5f
      Show excerpt
      nighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv [cs.LG], Jan. 2020. E. Mercado and S. Handel. Understanding the structure of humpback whale songs (l). The Jo
    • full textchunk-008
      text/plain3 KBdoc:agent/chunk-008/5506d265-7ff5-434b-b60e-b755c8a596d6
      Show excerpt
      Marine Science, 11:1394695, 2024. J. A. Allen, E. C. Garland, C. Garrigue, R. A. Dunlop, and M. J. Noad. Song complexity is maintained during inter-population cultural transmission of humpback whale songs. Scientific reports, 12(1): 8999, 2
    • full textchunk-007
      text/plain3 KBdoc:agent/chunk-007/04710b2a-ba75-48cb-94b5-13d951854faa
      Show excerpt
      atasets with thousands of classes can be high performing, even on out-of-domain down- stream tasks. Next, the ‘bittern lesson’ learned when training Perch 2.0 was that bird species classification in particular is a challenging su- pervision
    • full textchunk-006
      text/plain3 KBdoc:agent/chunk-006/44f49039-e92d-4aae-a989-a3343ce76194
      Show excerpt
      = 8k = 16k = 8 k = 16k = 8 k = 16 GMWM0.8900.9140.7640.8210.9360.9540.868* 0.917*0.8230.855 SurfPerch 0.9320.9470.8590.9030.9810.9840.7960.8990.982* 0.986* Perch 1.0 0.9580.9680.9010.9310.9770.9810.8360.9050.9580.970 Perch 2.0 0.9
    • full textchunk-005
      text/plain3 KBdoc:agent/chunk-005/31b9995b-056a-4dab-a3da-ede4fabae094
      Show excerpt
      V2.348 kHz3.0102420.0MBirds, Frogs AVES-bio16 kHzVariable768 2 94.4MGeneral Audio BirdAVES (large)16 kHzVariable1024 3 315.4MGeneral Audio + Birds 4 Comparison models. As our goal is to provide guidance on which pretrained embedding models
    • full textchunk-004
      text/plain3 KBdoc:agent/chunk-004/2ce1467e-29e9-40e4-a12c-ee1e34601ebc
      Show excerpt
      ludes new classes unseen by the models. The classes used in the NOAA PIPAN evaluation set include anthropomorphic noise, unknown whale species, and the following baleen whale species: common minke whale, humpback whale, sei whale, blue whal
    • full textchunk-003
      text/plain3 KBdoc:agent/chunk-003/05e7df2c-afdb-4b38-8576-118d1c22e948
      Show excerpt
      ained on log-mel spectrograms using a classification loss. Additionally, the model used a form of self-distillation and a self-supervised loss (in the form of source recording prediction) with the goal of producing strong embeddings that ar
    • full textchunk-002
      text/plain3 KBdoc:agent/chunk-002/6ad8a5fa-2898-42fc-95e1-ea78861375f7
      Show excerpt
      ion as new sounds are discovered while not having large amounts of human labeled data. Despite these challenges, passive acoustic monitoring is a critical tool for marine conservation and ecology (Fleishman et al., 2023), and discoveries ab
    • full textchunk-001
      text/plain3 KBdoc:agent/chunk-001/2b871fa0-4034-4d77-a1ce-b818711dd372
      Show excerpt
      Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind Abs
    • full textchunk-005
      text/plain3 KBdoc:agent/chunk-005/84c4d25d-a6fb-4da9-95ec-773c6e223fa2
      Show excerpt
      monitoring. Ecol. Inform., 61(101236):101236, Mar. 2021. 6 J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv [cs.LG], Jan. 2020
    • full textchunk-004
      text/plain6 KBdoc:agent/chunk-004/597f88dd-b871-4083-99cd-a9a4484853ab
      Show excerpt
      e datasets with thousands of classes can be high performing, even on out-of-domain down- stream tasks. Next, the ‘bittern lesson’ learned when training Perch 2.0 was that bird species classification in particular is a challenging su- pervis
    • full textchunk-003
      text/plain6 KBdoc:agent/chunk-003/e23b9efa-8e61-4312-a564-68c6956429b2
      Show excerpt
      ce on which pretrained embedding models should be used for agile modeling and transfer learning (with existing tools), we limit our comparisons to models supported in the Perch Hoplite Github repository 5 . We compare the performance of the
    • full textchunk-002
      text/plain6 KBdoc:agent/chunk-002/f0b400dc-caae-4eca-b34a-d5598b9eddf0
      Show excerpt
      l of producing strong embeddings that are linearly separable for a wide range of bioacoustics tasks. Embeddings from the Perch model have shown successful generalization to tasks other than species classification (e.g., individual identific
    • full textchunk-001
      text/plain6 KBdoc:agent/chunk-001/ae1f6e1d-0812-43e1-93c6-1e7778c77d74
      Show excerpt
      Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind Abs
    • full texttoiletpaper-smoke-paper
      application/pdf24 KBtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9
      Show excerpt
      Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind A

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.