Spatial and seasonal patterns of near-surface humidity in the foothills of the Canadian Rocky Mountains, 2005–2010
Bibliographic record
Abstract
Abstract. Near-surface humidity was monitored from 2005 to 2010 in a mesoscale network of 232 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18,000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. Hourly screen-level temperature and relative humidity were recorded over the study period, forming the basis for daily mean relative humidity and vapour pressure estimates. Hourly air pressure measurements at Calgary Airport are adjusted for elevation to calculate specific humidity from the vapour pressure. Daily mean specific humidity, relative humidity, and vapour pressure from the multi-year study are available at https://doi.pangaea.de/10.1594/PANGAEA.889435. This manuscript describes the processing methods used to quality-control and gap-fill the data. Overall data coverage for the study period is 89 %. Inverse-distance weighting techniques are used to estimate the missing 11 % of data, based on neighourhood values of daily mean specific humidity. We report monthly mean lapse rates of specific and relative humidity. Plots of seasonal and spatial humidity patterns illustrate the relations between humidity variables and temperature, elevation, and longitude in the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".