Development and evaluation of a Canadian agricultural ecodistrict climate database
Bibliographic record
Abstract
Spatially representative climate data are required input in various agricultural and environmental modelling studies. An agricultural ecodistrict climate database for Canada was developed from climate station data using a spatial interpolation procedure. This database includes daily maximum and minimum air temperatures, precipitation and incoming global solar radiation, which are necessary inputs for many agricultural modelling studies. The spatial interpolation procedure combines inverse distance squared weighting with the nearest neighbour approach. Cross-validation was performed to evaluate the accuracy of the interpolation procedure. In addition to some common error measurements, such as mean biased error and root mean square error, empirical probability distributions and accurate rates of precipitation occurrence were also examined. Results show that the magnitude of errors for this database was similar to those in other studies that used similar or different interpolation procedures. The average root mean square error (RMSE) was 1.7°C, 2.2°C and 3.8 mm for daily maximum and minimum temperature, and precipitation, respectively. The RMSE for solar radiation varied from 16 to 19% of the climate normal during April through September and from 21 to 28% of the climate normal during the remainder of the year.Key words: Maximum and minimum temperature, precipitation, solar radiation, ecodistrict, interpolation, cross validation
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".