EOF-MSE ADAPTIVE METHOD TO ASSESS AN ACID DEPOSITION MONITORING NETWORK OVER ALBERTA, CANADA
Bibliographic record
Abstract
This study provides an adaptive data analysis method that assesses Alberta's acid deposition monitoring network of 9 stations and the relative importance of each station. The method is based on the assessment of the mean square error (MSE) of sampling expressed in terms of empirical orthogonal functions (EOF). The annual potential acid input (PAI) data of the 9 stations over Alberta, Canada are used in this study. The patterns of the EOFs and PCs (principal components) are analyzed to reflect the PAI's spatial-temporal distribution properties. The definition and minimization of the MSE are the basis for our assessment method. The mean PAI field in the period of 1993–2006 and the PAI fields of individual years demonstrate a strong spatial inhomogeneity of the PAI field over Alberta. The PAI level is high in the Red Deer–Calgary–Kananaskis corridor. Our optimal analysis indicates that the 9-station network is, in general, adequate in monitoring the overall PAI in Alberta. The network results in a small root-mean-square-error/standard-deviation ratio (5.6%), which demonstrates the reasonable effectiveness of the network. In the period of 14 years (1993–2006), there were only three years (1993, 1998, and 2002) during which the PAI values were higher than the monitoring load of 0.17 [keq H+ ha-1yr-1] at three locations: Red Deer, Calgary, and Kananaskis. According to a station's contribution to the reduction of sampling error, the descending order of importance for the 9 stations is as follows: Beaverlodge, Fort Chipewyan, Suffield, Red Deer, Cold Lake, Kananaskis, Calgary, Fort Vermilion, and Fort McMurray.
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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) | 0.000 | 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".