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Record W2130895821 · doi:10.1002/etc.2963

Toxicity of fluoride to aquatic species and evaluation of toxicity modifying factors

2015· article· en· W2130895821 on OpenAlexafffund
Krysta Pearcy, James R. Elphick, Charlene Burnett‐Seidel

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsCameco (Canada)Nautilus Environmental
FundersCameco
KeywordsHyalella aztecaCeriodaphnia dubiaToxicityRainbow troutChronic toxicityAcute toxicityBiologyEnvironmental chemistryToxicologyEcotoxicologyAquatic toxicologyChemistryEcologyFisheryAmphipodaFish <Actinopterygii>Crustacean

Abstract

fetched live from OpenAlex

The present study was performed to investigate the toxicity of fluoride to a variety of freshwater aquatic organisms and to establish whether water quality variables contribute substantively to modifying its toxicity. Water hardness, chloride, and alkalinity were tested as possible toxicity modifying factors for fluoride using acute toxicity tests with Hyalella azteca and Oncorhynchus mykiss. Chloride appeared to be the major toxicity modifying factor for fluoride in these acute toxicity tests. The chronic toxicity of fluoride was evaluated with a variety of species, including 3 fish (Pimephales promelas, O. mykiss, and Salvelinus namaycush), 3 invertebrates (Ceriodaphnia dubia, H. azteca, and Chironomus dilutus), 1 plant (Lemna minor), and 1 alga (Pseudokirchneriella subcapitata). Hyalella azteca was the most sensitive species overall, and O. mykiss was the most sensitive species of fish. The role of chloride as a toxicity modifying factor was inconsistent between species in the chronic toxicity tests.

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.062
GPT teacher head0.288
Teacher spread0.226 · 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

Citations28
Published2015
Admission routes2
Has abstractyes

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