Anisotropic local constant smoothing for change-point regression\n function estimation
Bibliographic record
Abstract
Understanding forest fire spread in any region of Canada is critical to\npromoting forest health, and protecting human life and infrastructure.\nQuantifying fire spread from noisy images, where regions of a fire are\nseparated by change-point boundaries, is critical to faithfully estimating fire\nspread rates. In this research, we develop a statistically consistent smooth\nestimator that allows us to denoise fire spread imagery from micro-fire\nexperiments. We develop an anisotropic smoothing method for change-point data\nthat uses estimates of the underlying data generating process to inform\nsmoothing. We show that the anisotropic local constant regression estimator is\nconsistent with convergence rate $O\\left(n^{-1/{(q+2)}}\\right)$. We demonstrate\nits effectiveness on simulated one- and two-dimensional change-point data and\nfire spread imagery from micro-fire experiments.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".