MétaCan
Menu
Back to cohort
Record W2800062533 · doi:10.2166/wqrj.2018.035

The derivation of water quality criteria for bisphenol A for the protection of marine species in China

2018· article· en· W2800062533 on OpenAlexaff
Bo Guan, Lei Guo, Mark Gibson, Zhengyan Li

Bibliographic record

VenueWater Quality Research Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsDalhousie University
Fundersnot available
KeywordsReproductive toxicityWater qualityEnvironmental scienceChronic toxicityBiotaEnvironmental chemistryBiologyFisheryToxicologyEcologyToxicityChemistry

Abstract

fetched live from OpenAlex

Abstract Bisphenol A (BPA) is an environmental estrogen that occurs widely in the aquatic environment and causes feminization to various species, including fishes and gastropods. This study aims to develop the water quality criteria for BPA in the marine environment using the species sensitivity distribution (SSD) methodology from a scientific basis. Both acute and chronic toxicity data tested with saltwater species resident to China were collected. Additional tests were conducted to supplement toxicity data with local saltwater biota, including mollusk (Ruditapes philippinarum) and fish species (Scophthalmus maximus and Pagrosomus major). Based on SSD modelling, the criterion maximum concentration of BPA was estimated to be 273 μg/L. The criterion continuous concentration (CCC) for reproductive and non-reproductive effects was calculated to be 0.46 μg/L and 4.90 μg/L, respectively. Based on the derived criteria, the acute risk of BPA in coastal waters of China was determined to be negligible with RQs (risk quotients) of <0.01. The chronic risk was however much higher with RQs of up to 0.4 and 4.3 based on non-reproductive and reproductive CCC, respectively. The ecological risk assessment for BPA based on reproductive CCC can, therefore, better protect the safety of marine species.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.136
GPT teacher head0.488
Teacher spread0.352 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueWater Quality Research JournalSame topicEffects and risks of endocrine disrupting chemicalsFrench-language works237,207