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Record W1963482020 · doi:10.1029/2009jc005334

Atmospheric forcing of sea ice in Hudson Bay during the fall period, 1980–2005

2010· article· en· W1963482020 on OpenAlexaffabout
Klaus P. Hochheim, David G. Barber

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSea iceClimatologyArctic oscillationBayArctic ice packContext (archaeology)Environmental sciencePeriod (music)Sea ice concentrationGeologyOceanographySea ice thicknessNorthern Hemisphere

Abstract

fetched live from OpenAlex

The principal objective of this study is to describe the autumn sea ice regime of Hudson Bay in the context of atmospheric forcing from 1980 to 2005. Both gridded Canadian Ice Service (CIS) data and Passive Microwave (PMW) data are used to examine the freezeup period for weeks of year (WOY) 43–52. Sea ice concentration (SIC) anomalies reveal statistically significant trends, ranging from −23.3% to −26.9% per decade, during WOY 43–48 using the CIS data and trends ranging from −12.7% to −16.8% per decade during WOY 45–50 using the PMW data. Surface air temperature (SAT) anomalies are highly correlated with SIC anomalies ( r 2 = 0.52–0.72) and with sea ice extents ( r 2 = 0.53–0.72). CIS data show that mean sea ice extents based on SICs ≥80% (consolidated ice) have decreased by 1.05 × 10 5 to 1.17 × 10 5 km 2 for every 1°C increase in temperature in late November; PMW data show similar results. Regression analysis between SAT and standardized climate indices over the 1951–2005 period show that the East Pacific/North Pacific index is highly predictive of interannual SATs followed by the North Atlantic Oscillation and Arctic Oscillation indices. The data show that the Hudson Bay area has recently undergone a climate regime shift, in the mid 1990s, which has resulted in a significant reduction in sea ice during the freezeup period and that these changes appear to be related to atmospheric indices.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.265
Teacher spread0.252 · 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

Citations91
Published2010
Admission routes2
Has abstractyes

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