Gill morphology as an indicator of thermal adaptation and phenotypic \nplasticity in Atlantic salmon (Salmo salar)
Bibliographic record
Abstract
Climate change is a growing problem facing freshwater ecosystems, as water conditions \ncontinue to fluctuate with increasing intensity, and this is having major impacts on the \nbiodiversity of these habitats. For many fish species, including Atlantic salmon (Salmo salar), \nadaptations to this environmental variability will be critical to their long-term outlook. This study \nexplores how Atlantic salmon may respond to climate change and whether there is evidence that \nthey can adapt to warming water conditions. To accomplish this, I examined whether gill \nmorphometrics are an indicator of thermal tolerance in Atlantic salmon. Two potential drivers of \nthermal tolerance were assessed: thermal adaptation and phenotypic plasticity. Thermal \nadaptation is driven by genetic responses to the environment, which may evolve over \ngenerations, while phenotypic plasticity can occur over a much shorter period of time (i.e. within \nan individual's life span). Using three geographically separate populations that span a thermal \ngradient, Atlantic salmon gill metrics were compared and related to CTmax data to thereby assess \nthermal adaptation. To assess phenotypic plasticity, gills from one population were compared \nbefore and after exposure to prolonged warm temperatures. We found no significant evidence of \nthermal adaptation in gill morphology between populations and no relationship between CTmax \ntemperatures and gill morphology, as well as no evidence of any phenotypic plasticity occurring \nover the course of our experimental temperature challenge. However, there were plenty of \ninteresting trends in the data which will be discussed in depth later in this thesis. Overall, this \nstudy serves to help predict both future population-level and individual-level responses to \nclimate change in Atlantic salmon in Newfoundland.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".