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
Competition between crops and weeds is a complex phenomenon. Comprehensive, process-oriented simulation models which treat competition in a mechanistic rather than an empirical fashion, can offer insight into relationships among competition, crop and weed density, relative time of emergence, various morphological and physiological traits, and resource levels. They can also be used for prediction as part of a Systems approach to weed management. This paper reviews the features of a number of recent simulation models of crop-weed competition, the species for which they have been parameterized, and their applications. To date, these models have been used primarily to predict crop yield losses due to weed competition. Their ability to simulate weed seed production in response to the environment has not been exploited. The next step is to link simulation models of crop-weed competition to weed population dynamics models, in order to improve our ability to predict the effect of various weed management strategies over time. Advantages and drawbacks of a modeling approach to weed management problems are discussed.
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 | 0.000 | 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 | 0.000 | 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