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Record W3212390295 · doi:10.1002/essoar.10508727.1

Sea-ice impacts inter-annual variability in bloom phenology and carbon export

2021· preprint· en· W3212390295 on OpenAlexaff
Isabelle Giddy, Sarah Nicholson, Bastien Y. Queste, Sandy Thomalla, Sebastiaan Swart

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsCanadian Society of Intestinal Research
FundersKnut och Alice Wallenbergs StiftelseSwedish Foundation for International Cooperation in Research and Higher EducationNational Research Fund, KenyaVetenskapsrådetNeurosciences Research Foundation
KeywordsBloomWorld Wide WebOceanographyComputer scienceGeology

Abstract

fetched live from OpenAlex

Carbon export from the ocean surface to depth is an important component of the biological carbon pump, a key regulator of the world’s climate. The Antarctic Marginal Ice Zone accounts for 15% of Southern Ocean primary production, however, limited observations mean that the variability and drivers of primary production and its link to export are poorly constrained. Using a combination of gliders, biogeochemical argo floats and satellite observations, we show that sea-ice impacts both primary production and export through its influence on the upper ocean vertical density structure, light availability, and nutrient supply. Resultant changes in community composition, coupled with variations in vertical stratification, appear to be important determinants of carbon transfer to depth. The response of primary production and carbon export to sea-ice indicates that the biological carbon pump in this region is sensitive to ongoing climate change and predictions of reduced sea-ice cover in the future.

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)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.291
Teacher spread0.276 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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