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Record W7000464785

Field application of a model of proton and metal mixture bioavailability and effects

2023· other· en· W7000464785 on OpenAlexaboutno aff

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLimitingTable (database)Noise (video)Filter (signal processing)
DOInot available

Abstract

fetched live from OpenAlex

Quantitatively predicting of the responses of freshwater ecosystems to the effects of potentially toxic metals and acidification is an ongoing challenge in ecotoxicology. The WHAM-FTOX model is based on the assumptions that toxic effects of protons and metal cations are additively related to their occupancies of binding sites on organisms, and that those binding sites can be represented by the binding sites of humic acid (HA). We applied the model to simulate the species richness (nsp) of crustacean zooplankton in acid- and metal-contaminated lakes near Sudbury, Ontario between 1973 and 2006. Historic emissions from metal smelters at Sudbury have caused contamination of surrounding lakes by acid deposition, while lakes closest to the smelters were \nalso contaminated with metals, mainly Ni and Cu, and to lesser extents Zn, Cd, and Pb. Changes in water chemistry over the study period show partial recovery as a result of emission reductions. In application, binding of protons and metals to organisms is simulated by applying the WHAM7 model for each water sample. A combined dose term, FTOX,i, is computed for each species within a conceptual assemblage, assuming a distribution of species sensitivities to metals. The probability of finding the species in a sample is then computed from a fixed relationship with FTOX,i, and nsp is computed by summing these probabilities across all species. The distribution of species sensitivities is found by assuming them to be lognormally distributed and fitting their mean and standard deviation. The model was able to describe the variability in nsp well, with an R-squared value of 0.84 (p < 0.0001). Generally, the model \nreproduces the temporal patterns of nsp in individual lakes well, although it tends to underestimate the rate at which species richness increases as water chemistry recovers. The most important toxic cations were H, Al, Ni, and Cu, with a small contribution from Zn. The predicted contributions of protons and individual metals to toxicity generally showed declines over time in the contributions of protons and Al as recovery from contamination took place. However, in some cases the contribution of Ni increased over time, despite reductions on the dissolved concentration. This illustrates the potentially complex interplay among water chemistry variables determining metal exposure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.114
GPT teacher head0.372
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueNERC Open Research Archive (Natural Environment Research Council)French-language works237,207