Inderjit (editor) Weed Biology and Management, pp. 285-315. © 2003 Kluwer Academic Publishers, The Netherlands. WEED MANAGEMENT IN LOW-EXTERNAL-INPUT AND ORGANIC FARMING SYSTEMS
Bibliographic record
Abstract
During the past quarter century, growing numbers of government policy makers, scientists, consumers, and farmers have expressed concerns over the impacts of conventional farming practices on environmental quality, human health, and the economic viability of farm families and rural communities. Particularly in western Europe, and to a certain extent in the USA, Canada, and other countries, these concerns have begun to translate into changes in public policy, research priorities, and market opportunities that favor the development of low-external-input (LEI) and organic farming systems. For example, during the 1980s in Sweden, a>50% reduction in agricultural pesticide use was mandated and achieved through coordinated sets of regulations, research and extension education activities, and economic incentives (Bellinder et al., 1994; Matteson, 1995). Similar approaches have been initiated in other European countries (Matteson, 1995). Concurrently, consumer demand for organic crop and livestock products has grown 20 to 25 % per annum in the USA, many European countries, and Japan (Geier, 1998; Myers and Rorie, 2001). Sales of organic products in 2000 were estimated at $7.8 billion in the
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.049 |
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".