Bibliographic record
Abstract
Interest in more rational and objective approaches to weed management has increased considerably in Canada and elsewhere. Cost/benefit issues, environmental concerns, and the development of weed resistance to herbicides have cast doubt on the rationality and sustainability of prophylactic herbicide use. The concept of an economic threshold for weeds and the broader concept of integrated weed management have considerable potential as practical agronomic tools in Canadian crop production Systems. A large number of experiments have been conducted to determine the impact of weeds on crop yield, but the models developed from these studies have been put to little practical use. Constraints to the practical implementation of these concepts include a lack of realistic sampling procedures to assess the impact of weeds on crops over large areas, and a lack of information on the long-term implications of seed production by uncontrolled weeds. Weed ecologists conducting weed interference experiments should define their objectives better, and should provide guidelines on how their findings can be used at the farm level. Emphasis should be placed on the effects of the crop on the weed rather than the weed on the crop. There is also a need for greater coordination of research activities among weed ecologists. The establishment of standard protocols for long-term studies across locations and years would enhance the relevance and precision of weed interference models, and lead to the development of user- friendly decision support Systems specifically adapted to aiding rational weed management decisions in Canadian crop production Systems. The development of such Systems will be essential to the implementation of weed thresholds and integrated weed management.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".