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Record W2091345097 · doi:10.5194/bg-2-323-2005

Controls of the surface water partial pressure of CO <sub>2</sub> in the North Sea

2005· article· en· W2091345097 on OpenAlexaff
Helmuth Thomas, Yann Bozec, Khalid Elkalay, H. J. W. de Baar, Alberto Borges, Laure‐Sophie Schiettecatte

Bibliographic record

VenueBiogeosciences · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersEuropean Commission
KeywordsOceanographyStratification (seeds)Sink (geography)Mixed layerEnvironmental scienceWater columnSurface waterBloomSea surface temperatureClimatologyPartial pressureAtmospheric sciencesGeologyGeographyChemistryOxygenBiology

Abstract

fetched live from OpenAlex

Abstract. The seasonal variability of the partial pressure of CO2 (pCO2) has been investigated in the North Sea, a northwest European shelf sea. Based on a seasonal and high spatial resolution data set the main controlling factors - biological processes and temperature - have been identified and quantified. In the central and northern parts being a CO2-sink all year round, the biological control dominates the temperature control. In the southern part, the temperature control dominates the biological control at an annual scale, since the shallow water column prevents stronger net-CO2 removal from the surface layer due to the absence of seasonal stratification. The consequence is a reversal of the CO2 sea-to-air flux during the spring bloom period, the only time, when CO2 is taken up from the atmosphere in the southern region. Net community production in the mixed layer has been estimated to 4 mol C m-2 yr-1 with higher values (4.3 mol C m-2 yr-1) in the northern part and lower values in the southern part (2.6 mol C m-2 yr-1).

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.010
GPT teacher head0.192
Teacher spread0.181 · 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 designObservational
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

Citations94
Published2005
Admission routes1
Has abstractyes

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