Derivation of toxicity parameters from field data: Analysis of lake zooplankton species responses to metals and acidity
Bibliographic record
Abstract
• Long-term response to toxic metals and acidity varies among lake zooplankton species. • Data analysis with WHAM- F TOX allowed derivation of species-specific parameter β . • β values for lake zooplankton species fall within the range of β from laboratory data. The WHAM- F TOX β model describes the toxic effects of mixtures of protons and metal cations towards biological species, using a set of intrinsic parameters for the cations ( α H , α M *) and a sensitivity parameter ( β ) for each species. We applied the model to extensive water chemistry and zooplankton species occurrence data for four lakes contaminated with acidity and metals (Al, Ni, Cu, Zn) at Sudbury, Ontario, over the period 1973-2018, during which cation contamination declined, and zooplankton species numbers increased. Assuming that the appearance of a species resulted solely from decreases in water toxicity, and that α H and α M * values previously derived from laboratory toxicity test data could be applied in the field, we used the field data to estimate values of β for individual lake zooplankton species. Results for lake-species pairs with 20 or more species occurrences (from six samplings per year) were analysed. In most cases, the number of occurrences increased over time from zero to five or six per year, then remained at the high level. For a minority of pairs, occurrences per year increased initially, but subsequently declined, and so data only from the initial period were used to estimate β . The β values derived for the lake zooplankton are reasonably consistent with values derived from laboratory data for a range of other species. The findings support the application of WHAM- F TOX β to describe toxic effects of mixtures of cations in the field, and the toxicity model might be combined with ecological theory to interpret natural population responses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".