BirdAVES (large)

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

BirdAVES (large) has 17 facts recorded in Dontopedia across 1 reference, with 6 live disagreements.

17 facts·10 predicates·1 sources·6 in dispute

Mostly:has sample rate(2), has embedding dimension(2), has model parameter count(2)

Inbound mentions (1)

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.

`comparesPerformanceOf`Compares Performance of(1)

Model Comparisonex:model-comparison

Other facts (16)

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.

16 facts

Predicate	Value	Ref
Has Sample Rate	16000	[1]
Has Sample Rate	16	[1]
Has Embedding Dimension	1073741824	[1]
Has Embedding Dimension	1024	[1]
Has Model Parameter Count	315.4M	[1]
Has Model Parameter Count	315.4	[1]
Has Training Taxa	General Audio	[1]
Has Training Taxa	Birds	[1]
Tp:simulation Verdict	reproduced	[1]
Tp:simulation Verdict	contradicted	[1]
Tp:verdict Reason	The claimed model property matches the parsed model-property table in the staged manuscript.	[1]
Tp:verdict Reason	The claimed model property conflicts with the parsed model-property table in the staged manuscript.	[1]
Rdf:type	Model	[1]
Has Window Size	Variable	[1]
Trained on Taxa	General Audio Birds	[1]
Described in	Hagiwara 2023	[1]

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.

17 undated

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typetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:Model

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labeltp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

BirdAVES (large)

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hasSampleRatetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

16000

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hasWindowSizetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

Variable

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hasEmbeddingDimensiontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

1073741824

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hasModelParameterCounttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

315.4M

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trainedOnTaxatp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:general-audio-birds

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hasSampleRatetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

—

hasEmbeddingDimensiontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

1024

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hasModelParameterCounttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

315.4

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hasTrainingTaxatp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:general-audio

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hasTrainingTaxatp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:birds

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describedIntp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:hagiwara-2023

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simulationVerdicttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

reproduced

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verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

The claimed model property matches the parsed model-property table in the staged manuscript.

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simulationVerdicttp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

contradicted

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verdictReasontp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

The claimed model property conflicts with the parsed model-property table in the staged manuscript.

References (1)

1 references

[1]tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims17 facts
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…
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  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…
- full textchunk-007
  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…
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  text/plain3 KBdoc:agent/chunk-006/44f49039-e92d-4aae-a989-a3343ce76194
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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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  text/plain3 KBdoc:agent/chunk-005/31b9995b-056a-4dab-a3da-ede4fabae094
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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…
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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
  text/plain3 KBdoc:agent/chunk-002/6ad8a5fa-2898-42fc-95e1-ea78861375f7
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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…
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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…
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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
  text/plain6 KBdoc:agent/chunk-004/597f88dd-b871-4083-99cd-a9a4484853ab
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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
  text/plain6 KBdoc:agent/chunk-003/e23b9efa-8e61-4312-a564-68c6956429b2
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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…
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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…
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  text/plain6 KBdoc:agent/chunk-001/ae1f6e1d-0812-43e1-93c6-1e7778c77d74
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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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BirdAVES (large)

Inbound mentions (1)

comparesPerformanceOfCompares Performance of(1)

Other facts (16)

Timeline

References (1)

See also

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`comparesPerformanceOf`Compares Performance of(1)