Geostatistical mapping of precipitation: implications for rain gauge network design
Bibliographic record
Abstract
This study examined four univariate kriging techniques; simple kriging (SK), ordinary kriging (OK), multi-Gaussian kriging (MGC), and log-normal kriging (LNK); and two multivariate kriging algorithms; kriging with external drift (KED) using elevation and slope in two different models for the estimation of daily rainfall in a 250 m x 250 m grid over a 750 km2 area in the Canadian Boreal forest. Multivariate kriging did not enhance daily rainfall predictions. SK, OK, and LNK produced statistically comparative results with OK being slightly better. MGC was the worst univariate estimator, mainly due to the high percentage of data spikes. Sequential Gaussian simulation (SGS) was then implemented to produce 100 equiprobable maps of rainfall. A multi-objective approach; that is based on overlaying the map of the kriging variance, the DEM, and land use/land cover maps in a GIS framework to identify the areas of commonly favourable features; was proposed to identify potential future sampling locations.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".