Preserving Continuity of Long-Term Daily Maximum and Minimum Temperature Observations with Automation of Reference Climate Stations using Overlapping Data and Meteorological Conditions
Bibliographic record
Abstract
The seasonal-by-wind bias method for aligning time series of daily maximum and minimum temperatures from past conventional staffed and new automated sites using closely collocated, overlapping observations is presented for twenty-two modernized Reference Climate Stations in Canada. The method consists of adjusting for incompatible observing times and deriving biases from the daily "manual-minus-automated" temperature differences classified into seasons and wind-speed conditions. Most of the biases vary with the season, and many show limited wind dependency. Four sets of adjusted time series are prepared based on two-year and five-year overlapping data and on seasonal bias with or without wind conditions; the adjusted data are compared with the original observations. Based on the mean of the absolute differences and examination of box plots, the results show that, for this particular set of stations, the two-year versus five-year and seasonal versus seasonal-by-wind bias adjusted time series are overall similar. The largest contribution to the improvements in the adjusted observations came from matching the times of observation. Additionally, daily temperatures are adjusted using statistical methods applied with neighbouring station data but no overlapping observations at collocated stations; it is concluded that these do not necessarily resolve the bias between staffed and automated sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".