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Record W6946130877 · doi:10.26023/akwd-brv8-r80d

WINTRE-MIX: CFI Climate Sentinels Gault MRR-2 Processed Data. Version 1.0

2022· dataset· en· W6946130877 on OpenAlexaffabout

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsRadarPrecipitationClimate changeElevation (ballistics)Doppler radarProfiling (computer programming)

Abstract

fetched live from OpenAlex

This dataset contains post-processed data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Gault Nature Reserve (GAUL), about 30 km east of Montréal, Québec. The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located at the southern flank of Mont-Saint-Hilaire, a mountain with an elevation of about 400 m (above mean sea-level). Several other sites also collected MRR data during WINTRE-MIX.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.058

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 head0.338
Teacher spread0.313 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes2
Has abstractyes

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