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Record W2053526759 · doi:10.1021/es026366o

Effect of pH on Mercury Uptake by an Aquatic Bacterium:  Implications for Hg Cycling

2003· article· en· W2053526759 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Science & Technology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsMercury (programming language)BioaccumulationEnvironmental chemistryChemistryMethylmercuryMERCURECyclingDissolved organic carbonBacteriaInorganic chemistryAnalytical Chemistry (journal)Biology

Abstract

fetched live from OpenAlex

We studied the effect of increasing hydrogen ion (H+) concentration on the uptake of mercury (Hg(II)) by an aquatic bacterium. Even small changes in pH (7.3-6.3) resulted in large increases in Hg(II) uptake, in defined media. The increased rate of bioaccumulation was directly proportional to the concentration of H+ and could not be explained by assuming that the source of Hg to the bacteria was diffusion of neutrally charged species such as HgCl2. Thus, pH appeared to affect a facilitated mechanism by which Hg(II) is taken up by the cells. Lowering the pH of Hg solutions mixed together with natural dissolved organic carbon, or with whole lake water, also increased bacterial uptake of Hg(II). These findings have several potential implications for mercury cycling, including effects on elemental mercury production, mercury sedimentation, and microbial methylation of Hg(II), and could be part of the explanation for the observed positive correlation between lake acidity and methyl mercury levels in fish.

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.

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)0.001
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 score0.024
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.277
Teacher spread0.266 · 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