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Record W2809615095 · doi:10.1093/nsr/nwy066

Modelling marine DOC degradation time scales

2018· article· en· W2809615095 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

VenueNational Science Review · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsFisheries and Oceans CanadaMemorial University of Newfoundland
FundersNatural Environment Research CouncilSight Research UK
KeywordsDissolved organic carbonCarbon cycleDegradation (telecommunications)Environmental scienceCarbon fibersDeep seaEnvironmental chemistryClimate changeChemistryEcologyOceanographyGeologyBiologyEcosystemMathematicsComputer science

Abstract

fetched live from OpenAlex

Marine dissolved organic carbon (DOC) is formed of a large number of highly diverse molecules. Depending on the environmental conditions, a fraction of these molecules may become progressively resistant to bacterial degradation and accumulate in the ocean for extended time scales. This long-lived DOC (the so-called recalcitrant DOC, RDOC) is thought to play an important role in the global carbon cycle by sequestering carbon into the ocean interior and potentially affecting the climate. Despite this, RDOC formation is underrepresented in climate models. Here we propose a model formulation descripting DOC recalcitrance through two state variables: one representing the bulk DOC concentration and the other representing its degradability (k) which varies depending on the balance between the production of “new” DOC (assumed to be easily degradable) and bacterial DOC utilization assumed to leave behind more recalcitrant DOC. We propose this formulation as a means to include RDOC dynamics into climate model simulations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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