Fitness and genomic consequences of chronic copper and nickel exposure in «Daphnia pulex» mutation accumulation lines
Bibliographic record
Abstract
Anthropogenic pollution is a growing problem of serious environmental concern.Effects of pollutants are diverse, strongly pollutant-specific, and potentially include modification to the rate at which new mutations arise.Despite theory predicting that stressor-driven modifications of the mutation rate may be important in determining the evolutionary response of populations to stressors, few empirical studies of this phenomenon exist.Further, there are reasons to think mutation rates may generally be evolutionarily optimized within species and buffered from environmental effects.Empirical evidence is particularly lacking in multicellular organisms and this incomplete knowledge likely hinders effective management of wild populations exposed to stressors.To partially address this problem, I examined if chronic exposure to low levels of metals results in increased mutation rates in the water flea Daphnia pulex.Initially-geneticallyidentical lines of D. pulex were propagated under relaxed natural selection for over one hundred generations in the presence of sublethal concentrations of nickel and copper, or under metal-free control conditions.Assays of accumulated mutations were performed at the genotypic and phenotypic levels using whole genome sequencing and life-history approaches, FITNESS AND GENOMIC CONSEQUENCES
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".