A Mystery Tale: Nickel Is Fickle When Snails Fail—Investigating the Variability in Ni Toxicity to the Great Pond Snail
Bibliographic record
Abstract
Ni. This variability makes L. stagnalis either a moderately sensitive or the most sensitive freshwater species to chronic Ni exposure tested to date. Given the role of sensitive species in environmental risk assessment frameworks, it is particularly important to understand this variability, i.e., to characterize the factors that modulate Ni toxicity and that may confound toxicity test outcomes when uncontrolled. In the present study, we tested if this variability was due to analytical (growth calculation: biomass versus growth rate), environmental (water quality), lab-specific practices, and/or snail population differences among earlier studies. Specifically, we reanalyzed previously published Ni toxicity data and conducted additional measurements of Ni aqueous speciation, short-term Ni uptake, and chronic Ni toxicity with test waters and snail cultures used in previous studies. Corrections for Ni bioavailability and growth calculations explained a large degree of variability in the published literature. However, a residual 16-fold difference remained puzzling between 2 studies: Niyogi et al. (2014) (low ECxs) and Crémazy et al. (2018) (high ECxs). Indeed, differences in metal bioavailability due to water chemistry, lab-specific practices, and snail population sensitivity could not explain the large variation in Ni toxicity in these 2 very similar studies. Other potentially important toxicity-modifying factors were not directly evaluated in the present work: test duration, diet, snail holding conditions, and snail age at onset of testing. The present analysis highlights the need for further studies to elucidate 1) the mechanisms of growth inhibition in Ni-exposed L. stagnalis and 2) the important abiotic and biotic factors affecting this biological response. Until these processes are understood, substantial uncertainties will remain about inclusion of this species in Ni environmental risk assessment. Integr Environ Assess Manag 2020;16:983-997. © 2020 SETAC.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".