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Record W2064694905 · doi:10.3137/ao.v450105

Late‐summer pack ice in the Canadian archipelago: Thickness observations from a ship in transit

2007· article· en· W2064694905 on OpenAlexaffvenueabout
Jacqueline Dumas, Humfrey Melling, Gregory M. Flato

Bibliographic record

VenueATMOSPHERE-OCEAN · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCanadian Forest ServiceFisheries and Oceans CanadaUniversity of Victoria
FundersEurostars
KeywordsArchipelagoTransit (satellite)Physical geographyClimatologyGeologyMeteorologyEnvironmental scienceOceanographyGeographyEngineeringTransport engineeringPublic transport

Abstract

fetched live from OpenAlex

Abstract A digital video camera was used to photograph ice blocks turned on edge by the passage of the icebreaker CCGS Des Groseilliers operating in the Canadian High Arctic in August 2002. Ice thickness was derived from photogrammetry to an accuracy of about 10%, with a possible negative bias of about 3%. Further (presumably negative) bias related to route selection is unknown. The average thickness of blocks measured during half‐hour intervals of observation varied between 0.35 and 0.70 m; the higher values are likely indicative of second‐year ice. The greatest thickness of any single block was less than 2 m. Histograms of thickness were nearly symmetric and approximately Gaussian. There is evidence to indicate that the scarcity of ridged ice reflects the ice conditions of the area and is not an artefact of the method. Based on ancillary wintertime data, the pack ice in open waters of the north‐eastern Archipelago thawed at about 0.02 m d–1 in the summer of 2002, about three times less rapidly than ice near the shore. Whereas coastal ice had vanished five weeks prior to our voyage, pack ice in Norwegian Bay, though thin, persisted until the end of September and became second‐year ice. The difference is likely a consequence of the different microclimate near land, particularly with respect to the time of onset of the thaw season.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.870

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.221
Teacher spread0.195 · 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

Citations5
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicArctic and Antarctic ice dynamicsFrench-language works237,207