Provisional breeding zone determination modeled as a maximal covering location problem
Bibliographic record
Abstract
This study was a first attempt to model the problem of delineating breeding zones as a maximal covering location problem. The method involves two steps. First, a comprehensive set of candidate breeding zones is generated for a region using the focal point seed zone method. This method allows for control over the adaptive difference of genetic material within each zone. A grid of points is used to create the set of candidate breeding zones: one zone per point. Next, candidate zones are entered into a maximal covering location model formulated to suit this problem. The objective of this model is to select a subset of candidate zones that maximally covers the area of the region, given a limit on the number of zones to be selected and on the adaptive dissimilarity allowed within zones. Through use of this method, decision-makers can gain insight into how many breeding zones are needed to cover the region. Using different inputs from the focal point seed zone method, it is also possible to explore the trade-offs between the quantity and the quality of breeding zones. The method was tested on data from a series of jack pine (Pinus banksiana Lamb.) common garden trials of 102 seed sources from northwestern Ontario.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".