Bibliographic record
Abstract
While weed populations have traditionally been controlled by chemical and cultural methods, inundative biological control with microbial agents offers an additional strategy for managing weeds. Foliar pathogens have long been sought after as potential biocontrol agents, but rhizosphere microorganisms and their influence on weed growth and development have been ignored until recently. Rhizosphere soil is replete with a variety of microorganisms such as rhizobacteria, pathogenic soil-borne fungi, and arbuscular mycorrhizal fungi, all of which have a direct or indirect impact on weeds and their competitive ability. In some cases, specific microbes have a detrimental effect on the weeds and can be exploited as biological control agents. The ubiquitous mycorrhizal fungi are beneficial symbionts that can impart a competitive ad vantage to their plant hosts, particularly if mycorrhizal dependency is exhibited in weeds as opposed to crops. It may be possible to exploit various soil microbes by directly or indirectly reducing weed competition and tipping the competitive advantage in favor of the crop. However, information available on microbial/weed/crop relationships is limited and research efforts are required to explore the use of soil microorganisms as another weed management tool.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".