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Record W2979714555 · doi:10.1093/conphys/coz055

Influence of a dynamic rearing environment on development of metabolic phenotypes in age-0 Lake Sturgeon, Acipenser fulvescens

2019· article· en· W2979714555 on OpenAlexaff
Gwangseok R. Yoon, David Deslauriers, W. Gary Anderson

Bibliographic record

VenueConservation Physiology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of Manitoba
Fundersnot available
KeywordsLake sturgeonAcipenserBiologyOverwinteringSturgeonEcologyLarvaAnimal scienceZoologyFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Environment–phenotype interactions are the most pronounced during early life stages and can strongly influence metabolism and ultimately ecological fitness. In the present study, we examined the effect of temperature [ambient river temperature (ART) vs ART+2°C], dissolved oxygen (DO; 100% vs 80%) and substrate (presence vs absence) on standard metabolic rate, forced maximum metabolic rate and metabolic scope with Fulton’s condition factor (K), energy density (ED) and critical thermal maximum (CTmax) in age-0 Lake Sturgeon, Acipenser fulvescens, before and after a simulated overwintering event. We found that all the environmental variables strongly influenced survival, K, ED and CTmax. Fish reared in elevated temperature showed higher mortality and reduced K pre-winter at 127 days post-hatch (dph). Interestingly, we did not find any significant difference in terms of metabolic rate between treatments at both sampling points of pre- and post-winter. Long-term exposure to 80% DO reduced ED in Lake Sturgeon post-winter at 272 dph. Our data suggest that substrate should be removed at the onset of exogenous feeding to enhance the survival rate of age-0 Lake Sturgeon in the first year of life. Effects of early rearing environment during larval development on survival over winter are discussed with respect to successful recruitment of stock enhanced Lake Sturgeon, a species that is at risk throughout its natural range.

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 score0.172
Threshold uncertainty score0.501

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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