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Record W2160443845 · doi:10.1002/etc.5620210625

Evaluation of the effect of reactive sulfide on the acute toxicity of silver (I) to <i>Daphnia magna</i>. Part 1: Description of the chemical system

2002· article· en· W2160443845 on OpenAlexaff
Karl C. Bowles, Adalto Bianchini, Colin J. Brauner, James R. Kramer, Chris M. Wood

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2002
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDaphnia magnaSulfideSilver sulfideChemistryToxicityZinc sulfideEnvironmental chemistryZincAcute toxicityNuclear chemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Experiments were designed to assess the potential protective effect of the presence of sulfide against the acute (48-h) toxicity of silver(I) to Daphnia magna. Tests were conducted in borosilicate glass beakers (250 ml) in moderately hard synthetic water. Toxicity solutions were replaced after 24 h by static renewal method. This paper describes the chemical system, and the acute toxicity results are presented in a companion paper. Sulfide was below detection limit (<5 nM) in controls with no sulfide added. Sulfide, added as zinc sulfide clusters at approximately 35- or approximately 350-nM concentration, dropped in concentration to approximately 25 and 250 nM, respectively, over the 24-h period of measurements. Silver also decreased in concentration during the experiment (up to 59%), and the rate of loss was greater in the absence of sulfide compared with the presence of sulfide. A filtration experiment indicated a 1:1 binding ratio of silver to sulfide and a conditional stability constant for the Ag(I)-zinc sulfide complex of log K' = 8.9. The losses of sulfide and silver during the experiments highlighted the need for regular monitoring of the important chemical components of the system, even during short (48-h) 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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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

Citations23
Published2002
Admission routes1
Has abstractyes

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