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Record W4398397800 · doi:10.7910/dvn/cjymcv

Historical Snow Simulation (Open Loop)

2021· dataset· en· W4398397800 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

VenueHarvard Dataverse · 2021
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSnowLoop (graph theory)Open-loop controllerComputer scienceGeographyMeteorologyClosed loopMathematicsEngineeringControl engineeringCombinatorics

Abstract

fetched live from OpenAlex

This dataset contains historical snow water equivalent (SWE) simulations, produced from the Hydrotel snow module fed with meteorological observations. The simulations are provided on a 10km by 10km grid covering the southern portion of the province of Quebec, Canada and used as a proxy of SWE climatology in our adaptation of the Schaake shuffle.. The grid for the historical SWE simulation covers -81.5 to -57.1 in longitude and 43 to 53.4 in latitude, which is smaller than the meteorological grids, but for the the common portion, the grids overlap. The SWE grid has is 105 (lat) x 245 (Lon), for a total of 25725 pixels. A total of 44 years are used to produce the historical grids (1961-2004), and +/- 7 days around each of the date is used, for a total of 44years x 15 days = 660 values for each day of the year. Therefore, the dimensions of the historical SWE data is 660 x 25 725, which represents "nb of sample days x grid points).

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)0.000
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 score0.127
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1720.045

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 head0.045
GPT teacher head0.256
Teacher spread0.211 · 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