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Record W2104467958 · doi:10.1002/etc.5620191123

Silver uptake by the green alga <i>Chlamydomonas reinhardtii</i> in relation to chemical speciation: Influence of chloride

2000· article· en· W2104467958 on OpenAlexaff
Claude Fortin, Peter G. C. Campbell

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsChlamydomonas reinhardtiiGenetic algorithmChlamydomonasChlorideEnvironmental chemistryChlorophyceaeChemistryAlgaeChlorophytaBotanyBiologyEcologyBiochemistryOrganic chemistryGene

Abstract

fetched live from OpenAlex

Abstract Short-term (&amp;lt;1 h) silver uptake by the green alga Chlamydomonas reinhardtii was measured in the laboratory in defined inorganic media over a range of silver and chloride concentrations. For a low, fixed, free Ag+ concentration (e.g., 8 nM), silver uptake increases markedly (up to ∼4×) as a function of the chloride concentration (5 μM→4 mM Cl−); the free-ion model would have predicted a constant silver uptake rate. No evidence could be found for the passive diffusion of the neutral AgCl0 complex or for the facilitated uptake of the anionic AgCl−2 complex. The enhanced uptake observed in the presence of chloride is related to the very high silver uptake rates demonstrated by the test alga, which lead to diffusion limitation in the boundary layer surrounding the algal cell. In such a situation, metal accumulation is proportional to the total metal concentration (i.e., to the concentration gradient between the bulk solution and the algal surface). At higher silver concentrations (e.g., ≥ 10−7 M), diffusion in the phycosphere is no longer rate limiting, the chloride stimulation disappears, and silver uptake is sensitive to the free-ion concentration. However, such a high concentration of silver is not likely to be encountered in the environment, even in wastewater effluents.

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 categoriesInsufficient payload (model declined to judge)
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 score0.375
Threshold uncertainty score0.983

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.000

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 head0.003
GPT teacher head0.186
Teacher spread0.182 · 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.

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

Citations127
Published2000
Admission routes1
Has abstractyes

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