Comparison Study

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Comparison Study has 22 facts recorded in Dontopedia across 2 references, with 5 live disagreements.

22 facts·12 predicates·2 sources·5 in dispute

Mostly:expected to show(4), outcome condition(4), has goal(3)

Other facts (22)

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.

22 facts

Predicate	Value	Ref
Expected to Show	Higher Death Percentage Opium	[1]
Expected to Show	Lower Birth Percentage Opium	[1]
Expected to Show	Higher Criminal Percentage Opium	[1]
Expected to Show	Higher Suicide Percentage Opium	[1]
Outcome Condition	Habit Shortens Life Unproven	[1]
Outcome Condition	Habit Decreases Production Unproven	[1]
Outcome Condition	Habit Increases Crime Unproven	[1]
Outcome Condition	Habit Induces Suicide Unproven	[1]
Has Goal	Provide Guidance on Models	[2]
Has Goal	Agile Modeling	[2]
Has Goal	Transfer Learning	[2]
Tp:simulation Verdict	inconclusive	[2]
Tp:simulation Verdict	fragile	[2]
Tp:verdict Reason	The claim is source-grounded, but the unit's executable recomputation requirement is blocked by missing experiment artifacts.	[2]
Tp:verdict Reason	The manuscript names Hoplite and the public repository is reachable, but this does not provide the paper-specific raw evaluation artifacts.	[2]
Rdf:type	Analysis	[1]
Uses Data From	Statistical Information Singapore	[1]
Compares With	Other Quarters	[1]
Reference Group	Equally Conditioned Country	[1]
Uses Tools	Existing Tools	[2]
Limited to	Perch Hoplite Github Repository	[2]
Compares Performance of	Perch 2 0	[2]

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.

22 undated

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typeseven-sisters-of-sleep/151

ex:Analysis

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usesDataFromseven-sisters-of-sleep/151

ex:statistical-information-singapore

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comparesWithseven-sisters-of-sleep/151

ex:other-quarters

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expectedToShowseven-sisters-of-sleep/151

ex:higher-death-percentage-opium

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expectedToShowseven-sisters-of-sleep/151

ex:lower-birth-percentage-opium

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expectedToShowseven-sisters-of-sleep/151

ex:higher-criminal-percentage-opium

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expectedToShowseven-sisters-of-sleep/151

ex:higher-suicide-percentage-opium

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referenceGroupseven-sisters-of-sleep/151

ex:equally-conditioned-country

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outcomeConditionseven-sisters-of-sleep/151

ex:habit-shortens-life-unproven

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outcomeConditionseven-sisters-of-sleep/151

ex:habit-decreases-production-unproven

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outcomeConditionseven-sisters-of-sleep/151

ex:habit-increases-crime-unproven

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outcomeConditionseven-sisters-of-sleep/151

ex:habit-induces-suicide-unproven

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

ex:provide-guidance-on-models

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

ex:agile-modeling

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

ex:transfer-learning

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

ex:existing-tools

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

ex:perch-hoplite-github-repository

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

ex:perch-2-0

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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, but the unit's executable recomputation requirement is blocked by missing experiment artifacts.

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

fragile

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

The manuscript names Hoplite and the public repository is reachable, but this does not provide the paper-specific raw evaluation artifacts.

References (2)

2 references

[1]15112 facts
ctx:books/seven-sisters-of-sleep/151
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  crimes, and have established the fact—incomprehensive to our understandings, because we do not know the connecting links—that in every large country, the number of offences, and of each kind of offence, may be predicted for every coming yea…
[2]tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims10 facts
tp: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…
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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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  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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  = 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…
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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…
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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…
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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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  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
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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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Comparison Study

Other facts (22)

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References (2)

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