Evaluation Method

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Evaluation Method has 32 facts recorded in Dontopedia across 5 references, with 6 live disagreements.

32 facts·25 predicates·5 sources·6 in dispute

Mostly:tp:verdict reason(3), rdf:type(2), has step(2)

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.

`rdf:type`Rdf:type(4)

Percentage Calculationex:percentage-calculation
Precision Based Approachex:precision-based-approach
Rating Scale Assessmentex:rating-scale-assessment
Sample Based Validationex:sample-based-validation

`subTypeOf`Sub Type of(1)

Testing Processex:testing-process

Other facts (32)

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.

32 facts

Predicate	Value	Ref
Tp:verdict Reason	The claim is grounded in the staged manuscript text.	[5]
Tp:verdict Reason	The claim is source-grounded in the manuscript, but the artifact-availability requirement is blocked by missing exact code/model-card/data URLs.	[5]
Tp:verdict Reason	The claim is source-grounded, but the unit's executable recomputation requirement is blocked by missing experiment artifacts.	[5]
Rdf:type	Procedure	[1]
Rdf:type	Assessment Process	[4]
Has Step	Step 2	[1]
Has Step	Step 3	[1]
Has Window Size	5s	[5]
Has Window Size	3s	[5]
Applies Mean Pooling to	Noaa Pipan Dataset	[5]
Applies Mean Pooling to	Dclde Long Examples	[5]
Tp:simulation Verdict	reproduced	[5]
Tp:simulation Verdict	inconclusive	[5]
Type	comparative-analysis	[2]
Uses	diverse-term-set	[3]
Computes Embedding for	Recording	[5]
Uses Chunking	true	[5]
Has Hop Size	same as window	[5]
Embeds All Windows	true	[5]
Averages Embeddings	true	[5]
Condition for Averaging	recording duration exceeds window size	[5]
Selects Randomly	K Recording Embeddings	[5]
Trains	Logistic Regression Classifier	[5]
Computes Metric	One Vs All Roc Auc	[5]
Repeats Process	5	[5]
Uses Independently Sampled Sets	true	[5]
Reports	Average Roc Auc	[5]
Uses Few Shot Classification Setup	true	[5]
Uses Model Predictions Directly	false	[5]
Section Title	Evaluation method	[5]
Goal	Compare Embedding Performance	[5]
Established for	Weakly Labeled Data	[5]

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.

32 undated

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typebeam/d743eff9-5ab5-4843-9a74-f6d9d8afcc08

ex:Procedure

—

hasStepbeam/d743eff9-5ab5-4843-9a74-f6d9d8afcc08

ex:step-2

—

hasStepbeam/d743eff9-5ab5-4843-9a74-f6d9d8afcc08

ex:step-3

—

typebeam/399c8b34-603f-476b-bb60-24d48ee0b3ed

comparative-analysis

—

usesbeam/25045846-f0bb-4cc3-80b2-64502ed6702d

diverse-term-set

—

typebeam/5355a3f4-61dc-44b1-bfb9-44b0336b6344

ex:AssessmentProcess

—

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

ex:recording

—

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

true

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

—

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

—

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

same as window

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

true

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

true

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

recording duration exceeds window size

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

ex:noaa-pipan-dataset

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

ex:dclde-long-examples

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

ex:k-recording-embeddings

—

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

ex:logistic-regression-classifier

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

ex:one-vs-all-roc-auc

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

—

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

true

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

ex:average-roc-auc

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

true

—

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

false

—

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

Evaluation method

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

ex:compare-embedding-performance

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

ex:weakly-labeled-data

—

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

reproduced

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

The claim is grounded in the staged manuscript text.

—

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

inconclusive

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

The claim is source-grounded in the manuscript, but the artifact-availability requirement is blocked by missing exact code/model-card/data URLs.

—

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.

References (5)

5 references

[1]D743eff9 5ab5 4843 9a74 F6d9d8afcc083 facts
ctx:claims/beam/d743eff9-5ab5-4843-9a74-f6d9d8afcc08
- full textbeam-chunk
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  Show excerpt
  2. **Collect Real Data**: Run the script with actual data and collect real performance metrics. 3. **Compare Results**: Compare the results across different databases to make an informed decision. By following this approach, you can compre…
[2]399c8b34 603f 476b Bb60 24d48ee0b3ed1 fact
ctx:claims/beam/399c8b34-603f-476b-bb60-24d48ee0b3ed
- full textbeam-chunk
  text/plain1 KBdoc:beam/399c8b34-603f-476b-bb60-24d48ee0b3ed
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  ### Explanation 1. **Column Alignment**: The script ensures that both datasets have the same columns in the same order by sorting the columns. 2. **Whitespace and Formatting**: The script strips whitespace and converts strings to lowercase…
[3]25045846 F0bb 4cc3 80b2 64502ed6702d1 fact
ctx:claims/beam/25045846-f0bb-4cc3-80b2-64502ed6702d
- full textbeam-chunk
  text/plain1 KBdoc:beam/25045846-f0bb-4cc3-80b2-64502ed6702d
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  - Uses spaCy to generate context-aware expansions, which are particularly useful for technical terms. 4. **Combining Results**: - Combines all the results from the different approaches to provide a comprehensive set of synonyms. ###…
[4]5355a3f4 61dc 44b1 Bfb9 44b0336b63441 fact
ctx:claims/beam/5355a3f4-61dc-44b1-bfb9-44b0336b6344
- full textbeam-chunk
  text/plain1 KBdoc:beam/5355a3f4-61dc-44b1-bfb9-44b0336b6344
  Show excerpt
  Given your specific domain and the need to handle synonym mismatches effectively, **RoBERTa** or **BERT** are likely to be strong choices due to their robust context understanding capabilities. If computational resources are a concern, **Di…
[5]tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims26 facts
tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
- full textchunk-009
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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…
- 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…
- 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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  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…
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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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Evaluation Method

Inbound mentions (5)

rdf:typeRdf:type(4)

subTypeOfSub Type of(1)

Other facts (32)

Timeline

References (5)

See also

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`rdf:type`Rdf:type(4)

`subTypeOf`Sub Type of(1)