Climate impacts to inland fishes: Shifting research topics over time
Bibliographic record
Abstract
Climate change remains a primary threat to inland fishes and fisheries. Using topic modeling to examine trends and relationships across 36 years of scientific literature on documented and projected climate impacts to inland fish, we identify ten representative topics within this body of literature: assemblages, climate scenarios, distribution, climate drivers, population growth, invasive species, populations, phenology, physiology, and reproduction. These topics are largely similar to the output from artificial intelligence application (i.e., ChatGPT) search prompts, but with some key differences. The field of climate impacts on fish has seen dramatic growth since the mid-2000s with increasing popularity of topics related to drivers, assemblages, and phenology. The topics were generally well-dispersed with little overlap of common words, apart from phenology and reproduction which were closely clustered. Pairwise comparisons between topics revealed potential gaps in the literature including between reproduction and distribution and between physiology and phenology. A better understanding of these relationships can help capitalize on existing literature to inform conservation and sustainable management of inland fishes with a changing climate.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".