Apport de la technique du krigeage avec dérive externe pour une cartographie raisonnée de l'équivalent en eau de la neige : Application aux bassins de la rivière Gatineau
Bibliographic record
Abstract
The geostatistical algorithm of kriging with external drift (KED) is applied to the spatial estimation of snow water equivalent measured at single points. A digital elevation model with a 10-km resolution is used as external drift. Over the dense network of the period of interest (mid-March 1982), which corresponds to the maximum snow accumulation and the beginning of the snow melt in the Gatineau River basin, the KED technique is compared to the univariate ordinary kriging (OK). The results indicate a significant estimation precision improvement when the KED technique is used, notably in the under-sampled and extrapolated zones. A quantitative performance barometer — the root-mean-square (RMS) error — of this method with regards to the various degradation levels of the snow depth measurement network is proposed.Key words: snow water equivalent, kriging with external drift, root-mean-square errors, digital elevation model.[Journal translation]
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".