Estimation of Regionalized Phenomena by Geostatistical Methods: Lake Acidity on the Canadian Shield
Bibliographic record
Abstract
This paperdperA)j7 a geostatistical techniquebased oncond)WA`LG simulations to assess confidPLW intervals of local estimates of lake pH values on theCanad7( Shield This geostatistical approach has beendenA)PFF todA) with the estimation of phenomena with a spatial autocorrelation structure among observations. It uses the autocorrelation structure todA)G7 minimum-variance unbiased estimates for points that have not been measured or to estimate average values for new surfaces. A survey for lake water chemistry has beencondW(P) by the Ministred l'Environnementd Qubec between 1986 and 1990, to assess su face wate quality and dity A)G the a eas affected by acid p ecipitation on the southe n Canadana Shield in Qubec. The spatial st uctu e of lake pH was modA7Gj using two nested sphe ical va iog am modjWPA with anges of 20 km 250 km, accounting espectively fo 20% 55% of the spatial va iation, plus a andjW component accounting fo 25%. The pH d A) have been to const uct a numbe of geostatistical simulations that podLF( plausible ealizations of a given andn function modtio while `hono ing' the expe imental values (i.e., the eal points a e among the simulated dlated and that coespond to the same und lying va iog am modLjB Post-p ocessing of a la ge numbe of these simulations, that a e equally likely to occu , enables the estimation of mean pH values, the p opo tion of affected lakes (lakes with pH^5.5), and the potential error of these parameters within small regions (100 km!100 km). The method provid)W a procedA`L to establish whether acid rain control programs will succeed in redG7WL acid7WL in surface waters, allowing one to consid7 small areas with particular physiographic features rather than large drge AjW basins with several sources of heterogeneity. This jud AGjG on...
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".