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Record W176789055

Boreas AFM-07 Src Surface Meteorological Data

2013· book· en· W176789055 on OpenAlexaffabout
Heather Osborne, Forrest G. Hall, Jeffrey A. Newcomer, K. Young, Virginia Wittrock, Stan Shewchuck, David E. Smith

Bibliographic record

VenueNASA STI Repository (National Aeronautics and Space Administration) · 2013
Typebook
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsOak Ridge National LaboratorySuiteLongwaveRemote sensingData setMeteorologyASCIIEnvironmental scienceRidgeGeographyRadiationComputer scienceCartographyPhysicsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The Saskatchewan Research Council (SRC) collected surface meteorological and radiation data from December 1993 until December 1996. The data set comprises Suite A (meteorological and energy balance measurements) and Suite B (diffuse solar and longwave measurements) components. Suite A measurements were taken at each of ten sites, and Suite B measurements were made at five of the Suite A sites. The data cover an approximate area of 500 km (North-South) by 1000 km (East-West) (a large portion of northern Manitoba and northern Saskatchewan). The measurement network was designed to provide researchers with a sufficient record of near-surface meteorological and radiation measurements. The data are provided in tabular ASCII files, and were collected by Aircraft Flux and Meteorology (AFM)-7. The surface meteorological and radiation data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

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)0.001
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 score0.702
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.022

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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
Published2013
Admission routes2
Has abstractyes

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