Weed Science Research and Funding: A Call to Action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it