Climate data for field trials: onsite micro stations versus ClimateNA
Bibliographic record
Abstract
Ecological studies in diverse environmental fields require accurate climate data for point locations that are often distant from reliable public weather stations. “Onsite” micro weather stations can be established directly at research locations, but purchase, establishment, and maintenance costs and data gaps can limit their feasibility. Alternatively, climate data for point locations can be predicted from ClimateNA, a publicly available software package, but the prediction accuracy in remote and mountainous locations is uncertain. We compared ClimateNA predictions with observations from onsite weather stations located at 11 interior spruce provenance trials in British Columbia, Canada. We found that ClimateNA predictions were highly accurate for temperature variables but moderate for precipitation variables when compared with onsite weather data. Growth response functions developed with the two data sources showed similar shapes for temperature variables. Our results suggest that (1) temperature variables can be accurately predicted at remote and mountainous locations using ClimateNA; (2) precipitation variables are more accurately predicted with ClimateNA than with onsite weather stations, which are considerably affected by random factors; and (3) response functions provide an effective, independent tool to assess alternative sources of climate data. Our results recommend the use of ClimateNA over onsite weather stations, except where highly accurate precipitation data are required, in which case, high-quality onsite weather stations must be established and carefully maintained.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".