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Record W4312990968 · doi:10.46427/gold2022.12668

Stability of selenide in reducing waters - insights from chromatographic analyses

2022· article· en· W4312990968 on OpenAlexaff
Dirk Wallschlaeger

Bibliographic record

VenueGoldschmidt2022 abstracts · 2022
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSelenideStability (learning theory)ChromatographyChemistryComputer scienceMachine learningSeleniumOrganic chemistry

Abstract

fetched live from OpenAlex

Selenium (Se) is a contaminant of high environmental relevance in North America, due to its bioaccumulation potential in freshwater food chains impacted by industrial Se discharges. Bioreduction processes, both in ambient sediments and in industrial treatment processes, remove the Se oxyanions selenate Se(VI) and selenite Se(IV) from impacted waters, and convert them into reduced Se species, namely elemental Se(0) and selenite Se(-II). While Se(0) is considered to be not bioavailable, because it is not soluble in water, Se(-II) is of potential ecotoxicological concern, due to the possibility of it forming both volatile and organic Se species with high bioavailability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.294
Teacher spread0.242 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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