On the selection of an interpolation method with an application to the Fire Weather Index in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Evidence‐based studies in the environmental sciences frequently rely on the presence of spatially dense climatological data. However, such data are often available only at a fixed set of locations that may be regularly or irregularly arranged across a region. Spatial interpolation enables the approximation of variables of interest at locations between those sites. When conducting interpolation in collaboration with an end user or in interdisciplinary research, mutual knowledge exchange allows for greater insight on what is required of an interpolation method since each may have different pros and cons. We outline and discuss several key considerations one should make in an interpolation study, such as the purpose of the variable and the goals of the end user, including how the variable is used to inform decisions. This process is then illustrated via case study within a wildland fire weather context. For the province of Ontario, Canada, we contrast several methods for interpolating the Fire Weather Index (FWI), comparing them quantitatively via metrics and qualitatively using a proposed categorical gradients visualization scheme. Conditional simulations and a spatial ensemble are also investigated. This work is in collaboration with the Ontario Ministry of Natural Resources and Forestry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it