Bibliographic record
Abstract
Objectives: To test whether there is a relationship between characteristics of the journey to an outdoor cannabis cultivation site and the total number of plants grown. Methods: Spatial data on the location of a sample of 132 cultivation sites derived from aerial detection policing efforts is used. TwoStep cluster analysis is employed to derive profiles of cultivation sites based on three measures of distance (i.e., distance to road, to water, and elevation) and regression analysis is used to examine their implications for the number of plants grown. Results: Four types of cultivation sites are found: prime, rugged, dry, and remote. Prime sites are fairly close to roads and water sources and are at relatively low elevation. They grow the greatest number of plants (mean = 171). Low elevation is the single most important factor correlate of operation size. Further, remote sites (both further from road and at higher elevation) tend to be larger. Conclusions: A majority of growers are capable of identifying “prime” locations in which the tradeoff between rewards and security appears to be maximized. This study is limited by the fact that there was no information available on the offenders themselves. Future research should employ interviews to clarify decision-making processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".