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Noaa Pipan Dataset

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

Noaa Pipan Dataset has 25 facts recorded in Dontopedia across 1 reference, with 3 live disagreements.

25 facts·15 predicates·1 sources·3 in dispute

Mostly:has class(8), annotations derived from(3), rdfs:label(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdfs:labelin disputerdfs:label

  • NOAA PIPAN dataset[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • NOAA PIPAN[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Has Classin disputehasClass

  • Anthropomorphic Noise[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Blue Whale[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Brydes Whale[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Common Minke Whale[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Fin Whale[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Humpback Whale[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Sei Whale[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Unknown Whale Species[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Annotations Derived Fromin disputeannotationsDerivedFrom

  • Allen Et Al 2021[1]all time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Allen Et Al 2024[1]all time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
  • Noaa[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Lacks Specific TimestamplacksSpecificTimestamp

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

Has Weak Label TypehasWeakLabelType

Is Weakly LabeledisWeaklyLabeled

  • true[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Has Example LengthhasExampleLength

  • 30s[1]all time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Includes New ClassesincludesNewClasses

  • true[1]sourceall time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Has Recording SourcehasRecordingSource

Reported inreportedIn

Is Subset ofisSubsetOf

Used to EvaluateusedToEvaluate

  • Perch 2 0[1]all time · tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Inbound mentions (5)

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.

contributedToContributed to(2)

appliesMeanPoolingToApplies Mean Pooling to(1)

evaluatedOnEvaluated on(1)

providedAnnotationsForProvided Annotations for(1)

Other facts (3)

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.

3 facts
PredicateValueRef
Rdf:typeDataset[1]
Tp:verdict ReasonThe dataset or training/evaluation relationship is grounded in the staged manuscript.[1]
Tp:simulation Verdictreproduced[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.

annotationsDerivedFromtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:allen-et-al-2021
annotationsDerivedFromtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:allen-et-al-2024
annotationsDerivedFromtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:noaa
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:anthropomorphic-noise
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:blue-whale
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:brydes-whale
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:common-minke-whale
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:fin-whale
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:humpback-whale
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:sei-whale
hasClasstp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:unknown-whale-species
hasExampleLengthtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
30s
hasRecordingSourcetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:noaa-pacific-islands-fisheries-science-center-deployments
hasWeakLabelTypetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:file-level-label
includesNewClassestp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
true
isSubsetOftp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:noaa-passive-acoustic-archive
isWeaklyLabeledtp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
true
lacksSpecificTimestamptp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
true
labeltp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
NOAA PIPAN dataset
labeltp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
NOAA PIPAN
typetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:Dataset
reportedIntp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:noaa-pacific-islands-fisheries-science-center-2021
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.
usedToEvaluatetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
ex:perch-2-0

References (1)

1 references
  1. [1]chunk-00925 facts
    candidatetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
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      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
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      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
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      text/plain3 KBdoc:agent/chunk-007/04710b2a-ba75-48cb-94b5-13d951854faa
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      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
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      = 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
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      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
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      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
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      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
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      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
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      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
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      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
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      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
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      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
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      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
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      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
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      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

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