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Record W4231076798 · doi:10.5194/bg-2018-309

The export flux of particulate organic carbon derived from <sup>210</sup> Po /  <sup>210</sup> Pb disequilibria along the North Atlantic GEOTRACES GA01 (GEOVIDE) transect

2018· preprint· en· W4231076798 on OpenAlexaffabout
Yi Tang, Nolwenn Lemaitre, Maxi Castrillejo, Montserrat Roca‐Martí, Pere Masqué, Gillian Stewart

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsInstitute of Particle Physics
FundersDivision of Ocean SciencesCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsGeotracesFlux (metallurgy)ParticulatesTransectParticulate organic carbonSeawaterOceanographyBiological pumpEnvironmental scienceChemistryPhytoplanktonNutrientGeology

Abstract

fetched live from OpenAlex

Abstract. The disequilibrium between 210Po activity and 210Pb activity in seawater samples was determined along the GEOTRACES GA01 transect in the North Atlantic during the GEOVIDE cruise (May–June 2014). A steady-state model was used to quantify vertical export of particulate 210Po. The deficits of 210Po in the Iberian Basin and at the Greenland Shelf were strongly affected by vertical advection. Using the export flux of 210Po and the particulate organic carbon (POC) to 210Po ratio on total (> 1 µm) particles, we determined the POC export fluxes along the transect. Both the magnitude and efficiency of the estimated POC export flux from the surface ocean varied spatially within our study region. Export fluxes of POC ranged from negligible to 10 mmol C m−2 d−1, with enhanced POC export in the Labrador Sea. The cruise track was characterized by overall low POC export relative to net primary production (export efficiency

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 score0.123
Threshold uncertainty score0.244

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)0.0010.000

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 head0.013
GPT teacher head0.195
Teacher spread0.182 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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