Bibliographic record
Abstract
A review of modeling applied to vegetation management shows that these models range in resolution from simple yield equations to complex representations of processes affecting growth and competition for light, water, and nutrients. The latter models inform scientists and managers about mechanisms involved, while the former are more likely to be applied by managers to estimate effects of competition. Six generic model forms were identified. Models may be further categorized by whether they focus on weed population dynamics or on processes directly affecting growth of crop plants. There is scope for these aspects of vegetation modeling to be combined. Extremely complex models are scientifically satisfying as repositories of knowledge, but they tend to be excessively parametarized and recursive. Models with many parameters are difficult to fit to specific situations because they are ambiguous; the same overall estimates of growth can be achieved in a variety of ways. In addition, their recursive nature leads to compounded errors. Simple growth and yield models, by contrast, are usually very efficient and accurate at estimating local estimates of growth, but they are inadequately sensitive to the variety of site and site management practices that vegetation managers wish to represent. A new kind of hybrid model is proposed that combines the efficiency of forest mensurational techniques with sufficient complexity to represent the results of scientific studies associated with vegetation management for use by managers.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".