Weed Science Research and Funding: A Call to Action
Bibliographic record
Abstract
Weed science has contributed much to agriculture, forestry and natural resource management during its history. However, if it is to remain relevant as a scientific discipline, it is long past time for weed scientists to move beyond a dominating focus on herbicide efficacy testing and address the basic science underlying complex issues in vegetation management at many levels of biological organization currently being solved by others, such as invasion ecologists and molecular biologists. Weed science must not be circumscribed by a narrowly-defined set of tools but rather be seen as an integrating discipline. As a means of assessing current and future research interests and funding trends among weed scientists, the Weed Science Society of America conducted an online survey of its members in summer of 2007. There were 304 respondents out of a membership of 1330 at the time of the survey, a response rate of 23%. The largest group of respondents (41%) reported working on research problems primarily focused on herbicide efficacy and maintenance, funded mainly by private industry sources. Another smaller group of respondents (22%) reported focusing on research topics with a complex systems focus (such as invasion biology, ecosystem restoration, ecological weed management, and the genetics, molecular biology, and physiology of weedy traits), funded primarily by public sources. Increased cooperation between these complementary groups of scientists will be an essential step in making weed science increasingly relevant to the complex vegetation management issues of the 21st century.
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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 | 0.150 | 0.210 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.036 | 0.054 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.139 | 0.074 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".