Ecophysiological determinants of the variation in <sup>137</sup>Cs concentrations between and within lacustrine fish populations
Bibliographic record
Abstract
One decade after the Chernobyl fallout, the variability of 137Cs activity concentrations among fish within a Swedish lake was >20-fold based on 1361 individuals from seven species collected continually during 19961999. Of the total variability, 64% was due to differences between species but only 7% due to temporal variation, which was 1.3-fold for the whole community and 1.3- to 2-fold for population means. Contamination increased with body size (0.6- to 6-fold) and decreased with body condition in most species (1.3-fold). Body size and time together accounted for about half of the total variation within populations. Fish 137Cs was related to differences in feeding ecology, both between and within populations. Biomagnification factors ranged from 2.4 to 5.8. Contamination was highest in piscivorous populations and individuals, intermediate in herbivores and zooplanktivores, and lowest in fish specialized in benthic invertebrates despite their association with contaminated sediments. The 137Cs variance within populations was not correlated with their niche width but moderately positively correlated with fish trophic position and strongly positively correlated with functional omnivory (diversity in prey 137Cs). We conclude that individual resource specialization is an important source of variation in 137Cs concentrations within fish populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".