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Record W4379981868 · doi:10.1139/cjz-2022-0183

Short-term exposure to near-future CO<sub>2</sub>has limited influence on the energetics and behaviour of young-of-year salmonids

2023· article· en· W4379981868 on OpenAlexaffvenue
Emma M. Traynor, Simon W DePasquale, Caleb T. Hasler

Bibliographic record

VenueCanadian Journal of Zoology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsSalvelinusBiologyRainbow troutEnergeticsFontinalisTroutArcticMetabolic rateZoologySalmonidaeFisheryEcologyCarbon dioxideFreshwater ecosystemFish <Actinopterygii>Animal scienceEcosystem

Abstract

fetched live from OpenAlex

In many freshwater ecosystems, carbon dioxide (CO 2 ) is increasing. Unknown are the risks that high CO 2 poses for freshwater organisms, especially fish. The objective of this study was to determine how CO 2 may influence the growth rate, metabolic rate, feeding rate, and volitional behaviour of young-of-year Arctic charr ( Salvelinus alpinus (Linnaeus, 1758)), brook charr ( Salvelinus fontinalis (Mitchill, 1814)), and rainbow trout ( Oncorhynchus mykiss (Walbaum, 1792)). For this study, fish stayed in control (1100 microatmospheres (µatm)) or elevated (5236 µatm) CO 2 levels for 15 days. During this time, metabolic rate and behavioural tests were conducted on alternating days for each treatment. Weight and length of each fish were taken on days 0, 7, and 15. There was no evidence that elevated CO 2 affected the growth rate, feeding rate, or behaviour in any of these species. The standard metabolic rate in Arctic charr differed based on CO 2 exposure. Therefore, salmonids can withstand short periods of elevated CO 2 under these conditions. By comparing closely related species, the implications of this work are more ecologically relevant and will also help industry quantify the effects of high CO 2 on young salmonids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes2
Has abstractyes

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