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Record W6910900891 · doi:10.5061/dryad.05qfttdzj

Reactivity, fate and functional roles of dissolved organic matter in anoxic inland waters

2020· dataset· en· W6910900891 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversité du Québec à Montréal
FundersDeutsche Forschungsgemeinschaft
KeywordsNucleofectionGestational periodDiafiltrationTSG101ProteogenomicsHyporeflexiaLiquationDurvalumabFusible alloy

Abstract

fetched live from OpenAlex

The transit of organic matter (OM) through the aquatic compartment of its global cycle has been intensively studied, traditionally with a focus on the processing and degradation of its dissolved fraction (DOM). Because this is so intimately related to oxidation, the notion tenaciously persists that where dioxygen (O2) is absent, DOM turnover is markedly slowed. In this Opinion Piece, we outline how diverse processes shape, transform and degrade DOM also in anoxic aquatic environments, and we focus here on inland waters as a particular case study. A suite of biogeochemical DOM functions that have received comparatively little attention may only be expressed in anoxic conditions, and may result in enhanced biogeochemical roles of these deoxygenated habitats on a network scale.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0170.019

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 head0.029
GPT teacher head0.232
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→French-language works237,207→