MétaCan
Menu
Back to cohort
Record W6929116759 · doi:10.4224/12340979

Microstructure of first year sea ice ridges

2000· report· en· W6929116759 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2000
Typereport
Languageen
FieldMedicine
TopicHepatitis Viruses Studies and Epidemiology
Canadian institutionsNational Research Council CanadaCanadian Wood Council
Fundersnot available
KeywordsRidgeSea iceMicrostructureArctic ice packFast iceAntarctic sea icePancake iceSalinity

Abstract

fetched live from OpenAlex

First year sea ice ridges were characterized off the West Coast of Newfoundland from 07 to 22 March 1999. This report presents a comprehensive investigation of five ridges sampled during the March 1999 program. The ice microstructure of the ridges is correlated to the bulk physical properties (temperature, salinity and density) and consolidated layer thickness of the ridged ice. The ridges had a maximum sail height that ranged from 1.5 to 3.8 m. The consolidated layer thickness of the ridged ice ranged from 0.7 to 2.1 m and the total ice thickness varied from 1.8 to 5.0 m. The temperature of the ice cores was just below 0°C. The average bulk ice salinity ranged from 3.6 to 4.1 ‰, with a maximum of 6 ‰. Ice densities from the examined ridge site ranged from 0.85 to 0.93 Mg/m³. The warm temperatures, high porosity and low density of the ice indicated temperate ridges in a deteriorated state. Due to the deteriorated state of the ridged ice, it is expected that the measured average salinity of the ridges may have decreased after the ridge formed. The sail blocks showed that the ice involved in the ridge formation consisted of columnar grained ice, predominantly. Examination of the macrostructure of ridged ice cores showed highly porous, loosely consolidated ice with discrete banding. The microstructure of the cores revealed a non-uniform matrix comprised of mostly granular ice, with some coarse frazil particles and elongated columns.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.323
Teacher spread0.278 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2000
Admission routes3
Has abstractyes

Explore more

Same venueNPARCSame topicHepatitis Viruses Studies and EpidemiologyFrench-language works237,207