Using IT and GIS to Improve Crop Assesments
Bibliographic record
Abstract
Without attacking the issue of marijuana, it is practically impossible to meet the stated goals of the President's overall plan for decreasing illicit drug use. Within this context, this paper will examine the most authoritative data published by the U.S. government agencies that specialize in counter-narcotics issues. The objective of this paper is to describe how IT and GIS can help the drug policy community by providing possible better estimates of illegal crops. Pioneering work in imagery and crop estimation was done by the US Dept of Agriculture as far back as the 1930's. Archeologists use modern GIS techniques to develop areas of interest for historical digs. Specifically, a DSS design is presented, relying on three components: Functions necessary for the generation of a cueing layer, functions that interface with the Digital Mapping Server, and functions demanded by state agencies. The practicability of this approach has been demonstrated in a pilot project in the state of Mississippi, and is thus advocated. Deploying the Beta version of the model increased eradication efficiencies by an estimated 21% according to the lead Law Enforcement Agency using the technology in the state of Mississippi. Following this success, efforts are currently underway to deploy the technology in both the Appalachian region and the state of California — both high production areas of interest.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".