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Record W2897977828 · doi:10.1029/2018gl078995

Northward Expansion and Intensification of Phytoplankton Growth During the Early Ice‐Free Season in Arctic

2018· article· en· W2897977828 on OpenAlexafffund
Sophie Renaut, Emmanuel Devred, Marcel Babin

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaUniversité Laval
FundersAgence Nationale de la RechercheArcticNetCanada Excellence Research Chairs, Government of CanadaNational Aeronautics and Space Administration
KeywordsPhytoplanktonOceanographySea iceEnvironmental scienceArcticArctic ice packMarine ecosystemArctic sea ice declineSpring (device)Primary productivitySpring bloomClimatologyEcosystemGeologyDrift iceEcologyNutrient

Abstract

fetched live from OpenAlex

Abstract In the last decades, reduction of the ice cover has been documented to affect the structure and the functioning of Arctic marine ecosystems. One direct consequence is earlier phytoplankton spring blooms and larger annual primary production compared with previous decades. However, the impact of changes in the dynamics of sea ice specifically on phytoplankton spring blooms, a major contributor of the annual primary production in the Arctic Ocean, remains poorly known. Here we report on their temporal and spatial variabilities in open waters between 2003 and 2013 using satellite ocean color data. We observed a significant increase in primary productivity of phytoplankton spring blooms in different sectors of the Arctic Ocean, especially in the Barents and Kara Seas. Satellite observations also revealed a northward expansion of these blooms at a rate of 1° per decade driven by the Barents and the Kara regions.

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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.248
Teacher spread0.229 · 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

Citations70
Published2018
Admission routes2
Has abstractyes

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