Detecting hot spots of mountain pine beetle infestations in the forests of British Columbia: An approach using local spatial autocorrelation
Bibliographic record
Abstract
Mountain pine beetle (Dendroctonus ponderosae Hopkins) is an endemic species in the forests of British Columbia that has become epidemic and reached infestation levels like never before. Different approaches have been taken in order to try and manage the forest and understand the processes affecting the behavior of mountain pine beetle. No single model has been entirely successful in unearthing the complexity of mountain pine beetle behavior. In this thesis, large spatial data sets of mountain pine beetle attacks, obtained from helicopter and ground surveys, and further adjusted for the incorporation of uncertainty, are studied using a spatial autocorrelation approach in a pattern-based analysis. The study of spatial patterns is carried out by simulating possible scenarios of the observed data set. Moran’s I is used to obtain an overall measure of spatial autocorrelation of the global pattern and Local Indicators of Spatial Autocorrelation, specifically Local Moran’s I, are used to identify local pockets of high levels of infestation (hot spots). Using a significance criterion, regions that have intense infestations are screened to retain those that are more pervasive, thus having a more robust set of results that can be more reliable. Different levels of significance can be used to allow for a more ‘liberal’ or ‘strict’ screening of results. Study of the sensitivity of the data model and detection approach is carried out by comparing the locations of hot spots obtained with different detection methods. A comparison between results derived from data sets containing only aerial data and those containing aerial and field data is useful to determine the impact and effectiveness of sending crews to groundtruth aerial surveys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.000 | 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 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".