Perspectives on Designing Environmental Monitoring Networks for Measuring Extremes
Bibliographic record
Abstract
or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This paper discusses some of the problems and solutions associated with the monitoring the extremes generated by a random environmental field. One of these problems is the loss of spatial dependence (i.e. asymptotic independence) encountered in going from the original series of measurements to extreme values computed over fixed time intervals. The paper exhibits this loss in a case study involving particulate air pollution in Vancouver. That loss is characterized through simulation studies where the effect of increasing tail weight in the original is investigated. Overall, the problem raises concerns about the adequacy of modern environmental monitoring systems intended to protect human health against the impact of hazardous substances. This need for protection points to the difficult conceptual problem of selecting an appro-priate design criterion. Seemingly natural ways of interpreting that need lead to different objective functions and hence different designs. Side stepping this ambiguity by adopting a
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".