Bibliographic record
Abstract
In biostatistics and environmetrics, interest often centres around the development of models and methods for making inference on observed point patterns assumed to be generated by latent spatial or spatio-temporal processes. Such analyses, however, are challenging as these data are typically hierarchical with complex correlation structures. In instances where data are spatially aggregated by reporting region and rates are low, further complications may result from zero-inflation.\nIn this research, motivated by the analysis of spatio-temporal storm cell data, we generalize the Neyman-Scott parent-child process to account for hierarchical clustering. This is accomplished by allowing the parents to follow a log-Gaussian Cox process thereby incorporating correlation and facilitating inference at all levels of the hierarchy. A primary focus for these data is to jointly model storm cell detection and trajectories. To do so, storm cell duration, speed and direction are included in a marked point process framework. The thesis also proposes a general approach for the joint modelling of multivariate spatially aggregated point processes with the observed outcomes being zero-inflated count random variables. For such models, we incorporate correlation between the random field assumed to generate events and mean event counts. This is applied to lung and bronchus cancer incidence by public health unit in Ontario and a study of Comandra blister rust infection of lodgepole pine trees in British Columbia.\nThe key contributions from this thesis include the following: 1) developing a spatio-temporal hierarchical cluster process that incorporates correlation at all levels of the hierarchy, 2) joint modelling of a hierarchical cluster process and multivariate marks, 3) extending the framework for the joint modelling of multivariate lattice data to enable decomposition of the sources of shared spatial structure and 4) investigating aspects of the partial misspecification of joint spatial structure for multivariate lattice data.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".