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Record W4312071567 · doi:10.1002/aws2.1314

Comparison of anthracite and <scp>GAC</scp> biofilter performance for <scp>surface‐water</scp> manganese removal

2022· article· en· W4312071567 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

VenueAWWA Water Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsDalhousie University
FundersWater Research Foundation
KeywordsBiofilterAnthraciteEffluentManganeseEnvironmental engineeringChemistryEnvironmental scienceEnvironmental chemistryPulp and paper industryAcclimatizationBotanyBiologyCoalEngineering

Abstract

fetched live from OpenAlex

Abstract Alameda County Water District has observed increased biofilter effluent manganese concentrations during winter operations. To investigate manganese removal across surface water biofilters during cold‐water conditions, trends were analyzed between water temperature and manganese removal across multiple testing scales, multiple biofilter influent water qualities, and both anthracite and GAC media. During acclimation of new biofilters, 100% removal of manganese was observed sooner across both anthracite and GAC biofilters brought online at 20°C compared to 12°C. Acclimation at 12°C required 18 extra days for the GAC biofilters and 48 additional days for the anthracite biofilters. For fully acclimated biofilters, a decrease in manganese removal across both GAC and anthracite biofilters at temperatures below 15°C was observed. However, greater and more consistent manganese removal was observed across GAC compared to anthracite biofilters. Performance differences between locations also suggest that operational and water quality conditions also likely affect manganese removal.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.242
Teacher spread0.224 · 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