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Record W2175340938 · doi:10.1021/bk-2003-0835.ch002

Arsenic Speciation in Natural Waters

2002· book-chapter· en· W2175340938 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS symposium series · 2002
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsArsenicArseniteArsenateGenetic algorithmEnvironmental chemistryBiogeochemical cycleAbiotic componentArsenobetaineBiogeochemistryMetalloidSeawaterChemistryAquatic ecosystemEcologyBiologyMetal

Abstract

fetched live from OpenAlex

Speciation studies are necessary to understand the biogeochemical cycling of arsenic in aquatic systems. The species of arsenic present, their behaviour and toxicity will change depending on the biotic and abiotic conditions in the water. In groundwater, arsenic is predominantly present as arsenite (AsIII) and arsenate (AsV). The major arsenic species in freshwater are AsIII and AsV and minor amounts of MMA , DMA and methylated AsIII have also be detected. In seawater, the arsenic speciation differs in the surface and deep zone. In addition to the above species, uncharacterized arsenic species may constitute a significant portion of the total arsenic present in some water and the identification of these compounds is necessary to fully understand the arsenic biogeochemistry in water.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.804
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.012
GPT teacher head0.198
Teacher spread0.186 · 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