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Record W4254803533 · doi:10.1121/10.0001071.1

10.1121/10.0001071.1

2020· dataset· en· W4254803533 on OpenAlexaff
Amalis Riera, Rodney A. Rountree, Lucas Agagnier, Francis Juanes

Bibliographic record

VenueDefault Digital Object Group · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFisheryFish <Actinopterygii>BroadbandGeographyEnvironmental scienceOceanographyTelecommunicationsBiologyGeologyComputer science

Abstract

fetched live from OpenAlex

Sablefish sounds, named rasps, were recorded at two captive facilities in British Columbia and Washington State. Rasps consisted of highly variable broadband trains of 2 to 336 ticks that lasted between 74 and 10 500 ms. The 260 rasps that were measured contained frequencies between 344 and 34 000 Hz with an average peak frequency of 3409 Hz. The frequency structure of ticks within rasps was highly variable and included both positive and negative trends. This finding makes sablefish one of the few deep-sea fish for which sounds have been validated and described. The documentation of sablefish sounds will enable the use of passive acoustic monitoring methods in fisheries and ecological studies of this commercially important deep-sea fish.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.594
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.4060.650

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.229
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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