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Record W6964752302 · doi:10.26023/psc2-ttqs-390a

Cayenne NRC Convair 1D Data. Version 1.0

2017· dataset· en· W6964752302 on OpenAlexaffabout

Bibliographic record

VenueEarth Observing Laboratory · 2017
Typedataset
Languageen
FieldEnvironmental Science
TopicPlant Ecology and Soil Science
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsResearch councilIcingAltitude (triangle)Work (physics)

Abstract

fetched live from OpenAlex

This dataset contains 1D data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Cayenne, French Guiana. The data are from the NRC Convair 580, operated by the National Research Council Canada, for the flights flown during the project, and are in csv format. The 1D data was collected from the following instruments: Convair 580 geoid altitude, AIMMS20, Rosemount Icing Detector, Robust probe, IKP, FSSP, CDP, 2DS, and PIP.

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 score0.114
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0050.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0350.069

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 head0.261
Teacher spread0.235 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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