Adjusted Monthly Precipitation, Snowfall and Rainfall for Canada (1874-1990)
Bibliographic record
Abstract
this data set was distributed by nsidc until october 2003 when it was withdrawn from distribution because it duplicates the noaa national climatic data center ncdc data set td 9816 canadian monthly precipitation groisman p y 1998 national climatic data center data documentation for td 9816 canadian monthly precipitation national climatic data center 151 patton ave asheville nc 21 pp td 9816 contains monthly rainfall snowfall and precipitation the sum of rainfall and snowfall values from 6 692 stations in canada ncdc investigator pavel groisman obtained the original data from the canadian atmospheric environment service aes in the early 1990s and adjusted the measurements to account for inconsistencies and changes in instrumentation over the period of record td 9816 contains both the original and adjusted data related data are the historical adjusted climate database for canada version december 2002 and rehabilitated precipitation and homogenized temperature data sets provided by the climate monitoring and data interpretation division s climate research branch meteorological service of canada monthly rehabilitated precipitation and homogenized temperature data sets updated annually includes an alternative version of this data set using different correction methods it is distributed by the meteorological service of canada who also provides a microsoft word document that compares the two different data correction methods
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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 | 0.007 | 0.017 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".