Mapping the Population Density of Managed Honey Bee ( <i>Apis Mellifera</i> ) Colonies in Ontario, Canada: 2018
Bibliographic record
Abstract
Abstract Host population density as a risk factor for infectious disease transmission is an established concept in both host-parasite ecology and epidemiological disease modeling. A ‘population-at-risk’ value is a necessary denominator in epidemiological analyses to estimate absolute risk. However, local colony density values have been missing from published literature for Ontario, Canada, and crude density measures for the province do not consider the highly heterogeneous concentration of colonies in Southern Ontario. With geostatistical kriging methods, a continuous colony density map was developed from regionally aggregated apiary registration data. This study highlights the potential implications of colony population density on a macro scale and illustrates methodologies available to produce continuous density estimates over a given region with Ontario as an example. The estimation and mapping of continuous colony density values across the population provides future work with a source of data to further investigate potential associations of colony density and disease and helps to inform inspection and surveillance efforts. An interactive regional colony density map was also developed as a knowledge mobilization tool to increase the accessibility of these findings to members of the beekeeping community. The results of this study are an important practical step in advancing epidemiological research on managed honey bees and may lead to further development of strategies to improve the health of honey bees.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".