On Applying the Critical Thermal Maxima Method to Investigate Ecologically-Relevant Questions in Wild Fishes
Bibliographic record
Abstract
To the amazing fish and fishy-folks that I was fortunate enough to meet during the completion of this thesis, including my insightful supervisor Dr. Steven Cooke, who acted as a great mentor along the way.To Dr. Kim Birnie-Gauvin, who not only acted as a mentor, but who also had the chance to pull her hair out with me while struggling with a series of unanticipated events in Denmark -from hail storms to flooded waders, frozen feetses, bad jokes, and persistent bad luck.Thank you for all the laughs, discussions, dedication, and above all, putting up with all my crazy ideas over the past two years.To the other co-authors I had a chance to collaborate with -I have learned so much from every single one of you.The words of encouragement, insightful comments, and discussions we have had along the way have inspired me to pursue further work in this field.Finally, thank you to my friends, family, and partner in crime for being the source of my motivation and encouraging me even on
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".