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Record W2957840488 · doi:10.1139/as-2018-0034

Changing surface radiation and energy budgets of the Hudson Bay Complex using the North American regional reanalysis (NARR) model

2019· article· en· W2957840488 on OpenAlexafffundvenue
Richard Bello, Kaz Higuchi

Bibliographic record

VenueArctic Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsYork University
FundersDivision of Arctic SciencesYork University
KeywordsBayEnvironmental scienceClimatologyAlbedo (alchemy)Climate modelShoreSubmarine pipelineOceanographySea iceLatent heatClimate changeGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Monthly and annual component fluxes of the surface radiation and energy budgets for the two-decade period from 1997 to 2016 are compared with the climate normal period (1981–2010) for the marine system consisting of James Bay, Hudson Bay, Hudson Strait and Foxe Basin using estimates from the North American regional reanalysis model. Reflected solar radiation has declined unevenly, primarily offshore of major rivers, in polynyas and along shore leads, both during earlier melt and later freeze up. Annually, net radiation increases are driven by albedo decreases during the summer. Over 94% of the increases in ocean heat gain during the melt season are due to increases in absorbed sunlight. Large enhanced oceanic heat losses in the late fall are almost entirely consumed by intensified convective losses of both sensible and latent heat. All the seas within the Hudson Bay Complex show a reduced rate of ocean warming over the past two decades. This outcome can be partially reconciled with the observation that all water bodies are experiencing enhanced losses of energy during extended ice-free winters that exceed enhanced gains of energy during the extended ice-free summers. The implications of seasonal changes in ice cover for future climate are discussed.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.220
Teacher spread0.202 · 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 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

Citations9
Published2019
Admission routes3
Has abstractyes

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