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Record W4225403338 · doi:10.1242/jeb.244239

Characterizing the hypoxic performance of a fish using a new metric: <i>P</i>AAS-50

2022· article· en· W4225403338 on OpenAlexafffund
Yangfan Zhang, Daniel W. Montgomery, Connor F. White, Jeffrey G. Richards, Colin J. Brauner, Anthony P. Farrell

Bibliographic record

VenueJournal of Experimental Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRainbow troutLimitingFish <Actinopterygii>TroutMetric (unit)Hypoxia (environmental)StatisticsMathematicsOxygenEnvironmental scienceBiologyChemistryFisheryEngineeringOperations management

Abstract

fetched live from OpenAlex

The hypoxic constraint on peak oxygen uptake (ṀO2,peak) was characterized in rainbow trout over a range of ambient oxygen tensions with different testing protocols and statistical models. The best-fit model was selected using both statistical criteria (R2 and AIC) and the model's prediction of three anchor points for hypoxic performance: critical PO2 (Pcrit), maximum ṀO2 and a new metric, the minimum PO2 that supports 50% of absolute aerobic scope (PAAS-50). The best-fitting model was curvilinear using five strategically selected PO2 values. This model predicted PAAS-50 as 70 mmHg (coefficient of variation, CV=9%) for rainbow trout. Thus, while a five-point hypoxic performance curve can characterize the limiting effects of hypoxia in fish, as envisaged by Fry over 75 years ago, PAAS-50 is a promising metric to compare hypoxic constraints on performance in a standardized manner both within and across fish species.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.268
Teacher spread0.232 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations10
Published2022
Admission routes2
Has abstractyes

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