Kernel intensity estimation, bootstrapping and bandwidth selection for inhomogeneous point processes depending on spatial covariates
Bibliographic record
Abstract
In the point process context, kernel intensity estimation has been mainly restricted to exploratory analysis due to its lack of consistency. However the use of covariates has allow to design consistent alternatives under some restrictive assumptions. In this paper we focus our attention on de\-fi\-ning an appropriate framework to derive a consistent kernel intensity estimator using covariates, as well as a consistent smooth bootstrap procedure. For spatial point processes with covariates there is no specific bandwidth selector, hence, we define two new data-driven procedures specifically designed for this scenario: a rule-of-thumb and a plug-in bandwidth based on the bootstrap method previously introduced. A simulation study is accomplished to understand the behaviour of these procedures in finite samples. Finally, we apply the techniques to a real set of data made up of wildfires in Canada during June 2015, using meteorological information as covariates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".