What do we know about orographic precipitation gradients in mountainous areas? A comparative analysis in Canada, France, Sweden and Switzerland
Bibliographic record
Abstract
Precipitation estimation may turn out to be very difficult in mountainous regions, for three main reasons: (i) systematic undercatch of precipitation gauges (especially during snowfall), (ii) altitudinal (orographic) effects on precipitation, (iii) and sparser measuring networks (due to the remoteness of high elevation regions). These three elements are strongly correlated: the higher the altitude, the more uncertain the altitudinal relation between\naltitude and precipitation (low stations density) and the more important the underestimation of the precipitation (high quantity of snow).\nIn mountainous regions, the low density of the precipitation gauging network often causes numerous problems(Barry 1992). Among others, Fortin, Therrien et al. (2006) and Sevruk (2000) worked on the underestimation of the precipitation in measurements, due to the combination of snowfall and wind, while Johansson (2000) and Sevruk (1997) studied the relation between altitude and precipitation.\nThrough an analysis of orographic precipitation gradients in four different countries (Switzerland, Sweden, France and Québec), we try to identify ways to improve the estimation of precipitation input to catchments in mountainous areas, and more generally in snow affected areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".