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Record W6964734910 · doi:10.26023/68s9-0ebb-5a0d

WINTRE-MIX: Manual Hydrometeor Observation Reports. Version 1.0

2022· dataset· en· W6964734910 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsPrecipitationSampling (signal processing)Accretion (finance)MallinckrodtData sampling

Abstract

fetched live from OpenAlex

Manual observations were conducted to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) during 11 intensive observation periods (IOPs) between 01 Feb – 15 March 2022. Manual observations primarily include precipitation type, ice accretion thickness, and snowboard sampling measurements. These observations were conducted by research teams from the University at Albany, University of Colorado Boulder, Université du Québec à Montréal (UQAM), and McGill University. The research teams usually collected their manual observations throughout each IOP. Typically, the manual observations were conducted every 10 minutes at the same site as the soundings.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.639
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.6460.007

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 head0.026
GPT teacher head0.274
Teacher spread0.248 · 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