MétaCan
Menu
Back to cohort
Record W2527822101 · doi:10.1680/jenes.15.00021

Photochemical aqueous mercury removal: effects of DOM and DO

2016· article· en· W2527822101 on OpenAlexvenueno aff
Amy Gruss, Regina Rodriguez, Christine O. Valcarce, Erica W. Gonzaga, David W. Mazyck

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsMercury (programming language)ChemistryEnvironmental chemistryVolatilisationDissolved organic carbonOxygenNitrogenSulfurNitrateAqueous solutionUltravioletInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Photochemical reactions between mercury (Hg) and dissolved organic matter were studied to understand what conditions would promote mercury volatilisation from solution. Prepared solutions of mercury (II) nitrate and humic acid (HA) (at different ratios) were exposed to 254-nm ultraviolet irradiation and a continuous purge with nitrogen, air or oxygen gas to create three different dissolved oxygen (DO) concentrations. As HA was introduced into the system with a nitrogen purge, the overall quantity of dissolved gaseous mercury after 60 min, and subsequent mercury removal, decreased. An analysis of variance indicated that there is only a 61% confidence level that the 1:10 and the 1:100 mercury–HA ratios are statistically different, suggesting that the decrease in mercury removal as HA concentration increased cannot be solely attributed to mercury binding with sulfur or other functional groups on the HA. Experiments indicated a positive correlation of HA oxidation as DO increased with an air and oxygen purge.

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 score0.377
Threshold uncertainty score0.236

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)0.0000.000

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 head0.003
GPT teacher head0.196
Teacher spread0.192 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicMercury impact and mitigation studiesFrench-language works237,207