Bibliographic record
Abstract
Identification of the appropriate use rate is a critical first step in the herbicide development process because use rates affect product utility, market value, and the various risk assessments within the regulatory review process prior to registration. For a given herbicide to be commercially successful, it must provide consistent and sustained efficacy based on a use rate structure that meets customer requirements over a wide range of conditions. Recently, recommendations have been made that advocate the use of herbicide use rates below those outlined on registered product label text. Such advice tends to be based on field work and predictive models designed to identify specific conditions where reduced herbicide use rates are theoretically optimized as dictated by threshold values with assumed levels of commercially acceptable weed control. Unfortunately, many other studies indicate that the use of reduced herbicide rates is not without variability of herbicide efficacy and economic risk. Consequently, reduced use rate theories and related predictive models are often of limited practical value to growers. Aside from inconsistent performance, weed control strategies based on reduced herbicide use rates are not a solution to prevent or even delay target site resistance. In fact, prolonged use of sublethal use rates may select for metabolic resistance and add future weed management challenges by replenishing the weed seed bank. Much effort in terms of development time and resources are invested before product commercialization to ensure that product labels are easily understood and provide value to growers. In this regard, every effort is made to identify the lowest effective use rate that will consistently control target weeds and lead to economic optimization for both the grower and manufacturer.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".