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Record W2345436572 · doi:10.17895/ices.pub.25682376

Effects of end-of-the-century ocean acidification on Atlantic cod larvae of different populations in terms of survival, growth and recruitment to the fished stocks

2015· article· en· W2345436572 on OpenAlexaff
Martina Stiasny, Michael Sswat, R. Voss, Fredrik Jutfelt, Melissa Chierici, Velmurugu Puvanendran, Atle Mortensen, Thorsten B. H. Reusch, Catriona Clemmesen

Bibliographic record

VenueOpen MIND · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAtlantic codFisheryOcean acidificationOceanographyEnvironmental scienceLarvaBiologyEcologyClimate changeFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

No abstracts are to be cited without prior reference to the author.The effect of climate change on fish populations and fisheries is poorly understood. While fish may respond to increased temperatures with range shifts, they cannot escape ocean acidification, an inevitable consequence of increasing carbon dioxide concentrations in the oceans and in the atmosphere. Here, we tested the effect of ocean acidification (OA) on larvae of Atlantic cod of two different populations, from the Barents Sea and the Western Baltic Sea. Survival and growth was measured during the first weeks of development post-hatching. Mortality rates doubled in both populations in ocean acidification conditions as they are expected for the end of the century. When these results were included in a Ricker-type recruitment model, the recruitment collapsed. Furthermore the experimental results show an increase in larval size after seven weeks post-hatching. These results highlight the importance of including the effect of ocean acidification on vulnerable early life stages into fisheries models and management.

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 score0.033
Threshold uncertainty score0.164

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 head0.093
GPT teacher head0.304
Teacher spread0.211 · 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
Published2015
Admission routes1
Has abstractyes

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