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Record W1942444592 · doi:10.1002/2013eo110004

Bridging Time Scales, Disciplines, and Generations to Better Understand the Arctic Marine Ecosystem

2013· article· en· W1942444592 on OpenAlexaff
Alexandre Forest, Monika Kędra, Alexey K. Pavlov

Bibliographic record

VenueEos · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsArcticBiogeochemical cycleClimate changeEnvironmental scienceEcosystemBiodiversitySea iceEnvironmental resource managementThe arcticOcean acidificationOceanographyEarth scienceEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Understanding and predicting how ecological and biogeochemical processes in the Arctic Ocean are affected by global changes require an integrated approach. Modifications in the Arctic system may feed back to the Earth's climate, and shifts in food web functions could affect the people who depend on marine resources. Connecting information obtained along the circum‐Arctic, across disciplines and time scales as well as over generations, is thus key to gaining new insights on the interactions that drive the mechanics of change (Arctic in Rapid Transition Implementation Plan; http://www.iarc.uaf.edu/ART/implementation‐plan ). Such a framework is needed if the linkages between atmosphere‐ice‐ocean forcing, land‐ocean exchanges, biodiversity, and the productive capacity of the Arctic Ocean are to be properly understood.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.273
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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