MétaCan
Menu
Back to cohort
Record W7011469330

Meteorological characteristics of the Canadian Arctic Shelf Exchange Study

2008· dissertation· en· W7011469330 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2008
Typedissertation
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticSea surface temperatureSea ice
DOInot available

Abstract

fetched live from OpenAlex

The CASES meteorological program began October 22,2003 and continued until June 20, 2004 (day 295 to day 172, herein "observational period " or "study period").The prograrn consisted of rnanual meteorological sulface observations taken on the hour, atmospheric profiling of temperature, moisture and low-boundary layer winds, continuous cloud base height, integrated column water vapor and cloud liquid water measurements, as well as automated visibility observations.Few continuous meteorological data sets have been made during the fall-winter-spring period in the Southern Beaufort Sea sea-ice environment.Presented here is an overview of the hemispheric, synoptic and local scale meteorology observed from the CCGS Amundsen during the observational period.First, NCEP I reanalysis data were used to examine how the CASES year compared to the 1971-2000 3O-year "nomal".Second, surface 3-hourly isobaric analysis were used to track low-pressure disturbances (LPDs) affecting the CASES region during the observational period to reveal their location of origin and evolution.Third, a monthly (and overall) climatology of several meteorological variables (e.g.cloud type/amount/ceiling, adverse weather, wind, temperature) was produced.Finally, a comparison of data collected from the ship with surrounding Meteorological Service of Canada station data and an investigation into the accuracy of daily averages from NCEP I and NCEP II reanalysis data, North American Regional Reanalysis (NARR) and GEM-Regional analysis data is presented.The investigations reveal the weaknesses and strengths of popular and widely used model data with respect to actual station data with the intentions of improving atmospheric modeling in arctic regions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.208
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2008
Admission routes1
Has abstractno

Explore more

Same venueMspace (University of Manitoba)Same topicComputability, Logic, AI AlgorithmsFrench-language works237,207