An Imagery-Based Weed Cover Threshold Established Using Expert Knowledge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The implementation of site-specific weed management requires information about weed cover and decision support systems to determine weed cover thresholds and concomitant herbicide rates. Although it is possible to create accurate weed cover maps over large areas, weed cover thresholds have generally been evaluated using tedious weed density counts. To bridge this gap between weed cover obtained by machine vision and the concept of economic threshold, crop advisers specializing in weed scouting were asked to evaluate over 2,500 weed cover images (2 m by 3 m) and determine if a given image would require herbicide application or not. Using the area under the “receiver operating characteristic” curve method, an optimal weed cover threshold was established. The derived economic thresholds ranged from 0.06 to 0.31% weed cover contingent on the level of tolerance of the expert adviser. Although this threshold seems low, it is comparable with economic threshold values based on weed density.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it