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Record W4401120678 · doi:10.1039/d4ew00490f

Emerging investigator series: mitigation of harmful algal blooms by electrochemical ozonation: from bench-scale studies to field applications

2024· article· en· W4401120678 on OpenAlexaff
Shasha Yang, Estefanny Quispe-Cardenas, Athkia Fariha, Nada Shetewi, Victor Melgarejo Cazares, Nanyang Yang, Lewis McCaffrey, Nicole Wright, Michael R. Twiss, Siwen Wang, Stefan Grimberg, Yang Yang

Bibliographic record

VenueEnvironmental Science Water Research & Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsAlgoma University
FundersSmall Business Innovative Research and Small Business Technology TransferNew York State Department of Environmental Conservation
KeywordsAlgal bloomEnvironmental scienceCyanobacteriaMicrocystinEnvironmental chemistryScale (ratio)Environmental engineeringEcologyChemistryBiologyPhytoplanktonGeography

Abstract

fetched live from OpenAlex

Harmful algal blooms threaten ecosystems and water safety, necessitating rapid treatment strategies. This study developed an electrochemical ozonation process to realize simultaneous cyanobacteria inactivation and microcystin 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0440.013

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 head0.297
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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