Bibliographic record
Abstract
While the details of climate change and global warming remain subjects of intense debate, there is a growing awareness that the earth's temperature regime is changing, as a result of either natural variation or anthropogenic emissions of ‘greenhouse gases’. The effects of warming scenarios on agriculture, human health and vegetation have been extensively studied over the past decade, but there has been remarkably little research on the anticipated effects on fish and fisheries. This is somewhat surprising, given the fact that temperature is undoubtedly the most thoroughly studied environmental variable in fish biology! Fish are poikilotherms living in a medium of high heat capacity and conductance; their body temperature is normally within a few fractions of a degree of the water, and therefore the rates of all of their biological functions are critically dependent on environmental temperature. A few years ago, as we started our own research programme in this area, we came to realize that there existed only a modest amount of information on the long-term effects of small temperature increments on fish. Nonetheless, there was also clearly available a wealth of classical data on ‘temperature effects’ from which extrapolation and speculation could be made. We thought it would be useful to bring together experts from all areas of fish biology, and ask them to summarize the existing information, to identify significant gaps in current understanding, and to speculate, based on their knowledge of the field, on the responses of fish to chronic small increments in temperature superimposed on natural regimes.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.383 | 0.232 |
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".