MétaCan
Menu
Back to cohort
Record W2154981371 · doi:10.1007/978-0-933876-68-2_12

The Application of Fred Sanders’ Teaching to Current Research on Extreme Cold-Season Precipitation Events in the Saint Lawrence River Valley Region

2008· book-chapter· en· W2154981371 on OpenAlexaff
John R. Gyakum

Bibliographic record

VenueAmerican Meteorological Society eBooks · 2008
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrecipitationPrecipitable waterAnomaly (physics)SAINTClimatologyCurrent (fluid)Environmental scienceGeologyMeteorologyGeographyOceanographyHistoryArt history

Abstract

fetched live from OpenAlex

This research focuses on analyses of Saint Lawrence valley heavy precipitation events. Synoptic- and planetary-scale circulation anomaly precursors are typically identified several days prior to these events. These precursors include transient upper-level troughs, strong moisture transports into the region, and anomalously large precipitable water amounts. The physical insight of Fred Sanders’ work is used in the analysis of these composite results. Further details of this insight are provided in analyses of one case of heavy precipitation.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.321
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 designOther design
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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Meteorological Society eBooksSame topicMeteorological Phenomena and SimulationsFrench-language works237,207