Global Synoptic Climatology Network (GSCN)
Bibliographic record
Abstract
dataset dsi 9290 is the result of a joint effort to create a global synoptic climatology network among the meteorological service of canada downsview ontario and vancouver british columbia research institute for hydrometeorological information of the russian state committee for hydrometeorology obninsk russian federation and noaa national climatic data center subset 9290a is a compilation of in situ hourly meteorological observations for canada from approximately 170 active stations which can be operationally updated from the entire list of 768 locations and another 350 stations are updatable with a delay the maximum period of the data span is from january 1 1953 to february 21 2005 subset 9290c is a compilation of in situ hourly meteorological observations for the former ussr the number of stations in subset 9290c varies over the period of record with as many as 2095 stations in the entire former ussr prior to 1991 much of the most recent data is primarily from russia now the russian federation the maximum period of the data span is from january 1 1871 to january 1 2001 for both subsets the data variables include sea level and station pressure surface air temperature water vapor pressure relative humidity wind speed and direction several characteristics of cloudiness and present weather
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.030 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".