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Record W2121888854 · doi:10.1139/cjz-2013-0223

Critical thermal maximum (CT<sub>max</sub>) and hematology of shortnose sturgeons (<i>Acipenser brevirostrum</i>) acclimated to three temperatures

2014· article· en· W2121888854 on OpenAlexafffundvenue
Yueyang Zhang, James D. Kieffer

Bibliographic record

VenueCanadian Journal of Zoology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyHematocritAcclimatizationSturgeonCritical thermal maximumAcipenserHematologyFish <Actinopterygii>SalinityAnimal scienceFisheryInternal medicineZoologyEndocrinologyEcologyImmunologyMedicine

Abstract

fetched live from OpenAlex

Quantifying a species thermal tolerance is critical to assessing biological impacts of anticipated increases in temperature (e.g., climate change). Although many studies have documented the critical thermal maximum (CT max ) of fish, there is a paucity of research on thermal biology of sturgeon. The shortnose sturgeon (Acipenser brevirostrum LeSueur, 1818) is a threatened species that exists along the eastern coast of North America. They can be exposed to temperatures ranging from freezing to above 25 °C. Using a heating rate of 6 °C/h, CT max and the associated hematological responses of shortnose sturgeon acclimated to 10, 15, and 20 °C were determined. There was a significant positive relationship between CT max and body mass, and CT max increased significantly with increases in acclimation temperature (T a ). In general, hematology of thermally stressed fish was modified relative to control (nonstressed) fish. Hematocrit, plasma lactate, and plasma Na + and Cl – of fish were all significantly influenced by thermal stress and T a . Glucose and K + were only significantly influenced by thermal stress. Future studies should address the importance of other stressors, such as salinity and toxicants, on thermal relationships of sturgeon.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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

Citations76
Published2014
Admission routes3
Has abstractyes

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