Celebrating recent innovations in the application of stable isotopes to fish biology
Bibliographic record
Abstract
Celebrating recent innovations in the application of stable isotopes to fish biologyIt is over 50 years since the distribution of carbon and nitrogen stable isotopes (δ 13 C and δ 15 N) was first described in animal tissues (DeNiro & Epstein, 1978, 1981;Fry et al., 1978;Parker, 1964;Schroeder, 1983;Schoeninger & DeNiro, 1984).For fishes, this catalyzed a rich history of studies assessing trophic relationships, movement and migration, and physiological status across freshwater, brackish, and marine environments (Boecklen et al., 2011;Hansson et al., 1997;Shipley & Matich, 2020).As technological advancements continue to improve analytical capabilities, isotopic techniques will continue to transform the field of fish biology.This special issue celebrates some of these recent innovations with a focus on four broad themes: (1) constraining patterns of isotopic discrimination, (2) determining drivers of energy distribution across diverse fish communities, (3) assessing the impacts of human alteration on foraging and fitness, and (4) nontraditional stable isotope systems.The goal of this collection is to showcase state-of-the-art approaches that can improve our understanding of fish ecology and physiology in a changing world.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".