Impact of misspecifying spatial exposures in a generalized additive modeling framework: with application to the study of the dynamics of Comandra blister rust in British Columbia
Bibliographic record
Abstract
In environmental and epidemiological studies, the nearest distance between the susceptible subject and the exposure source is a commonly used exposure measure, principally because this measure is easy to collect; more recently, the density of the exposure has been considered as a measure of exposure. However, no study has ever quantitatively compared nearest distance and density of exposures in any field. In particular, in the field of forestry, few studies have accounted for density‐based exposure measures to disease pathogen, mostly due to the difficulty of measuring the spatial locations of disease host plants. Misspecification of exposure measures may result in inaccurate determinations of the link between exposure and the response of interest. Such considerations are motivated by the study of the disease dynamics of Comandra blister rust (Cronartium comandrae) on lodgepole pine. This disease spreads to pine trees through alternate host plants near the trees. We aim at understanding the relationship between the alternate host plant presence and the disease, as well as effects relating to genetic variation in the trees. We contrast the use of nearest distance to the alternate host plant, with host plant densities at different orders of neighborhood, as exposure measures, in the framework of a flexible semi‐parametric generalized additive model, while adjusting for a spatially smooth surface. We demonstrate that if exposure is inaccurately modeled, then bias in estimating genetic effects may manifest themselves and larger predictive error may be induced. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".