Using the “mean temperature of the catch” to assess fish community responses to warming in a temperate lake
Bibliographic record
Abstract
Abstract This contribution applies the “mean temperature of the catch” (MTC) concept of Cheung et al. (Nature 497:365–368, 2013) to fish catch data for Lake Peipsi, Estonia/Russia, covering the years 1931 to 2019. The preferred temperature of each of the ten target fish species was obtained from the literature, and combined with the species-specific catch data to obtain MTC values for each year. The analysis of the MTC time series thus obtained with a segmented regression yielded two trend lines, one horizontal at 14.5 °C (1931–1986), and the other (1987–2019) ascending with a slope 0.85 °C·decade−1. Overall, the segmented regression model explains over half of the variance of the MTC data set (multiple R2 = 0.53; adjusted R2 = 0.51). Lake surface water temperatures correlate with MTC, even though weakly (r = 0.30), when considering a 2-year time lag. The fish community of the shallow Lake Peipsi reacts more strongly to temperature changes than marine ecosystems so far studied using the MTC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".