Bibliographic record
Abstract
Annual rotation green manure crops of selected brassicas, buckwheat, forage pearl millet, forage radish, black-eyed Susan, sesame, sudangrass, and velvetbean were evaluated to determine impacts on lesion nematode Pratylenchus penetrans and dagger nematode Xiphinema americanum populations densities. Canadian forage pearl millet ‘101’and ‘Tifgrain 102’ millet effectively controlled P. penetrans but increased population densities of X. americanum. Black-eyed Susan (Rudbeckia hirta) and sudangrass ‘Trudan 8’ also reduced P. penetrans densites but not X. americanum. Rapeseed and other brassicas as a green manure reduced X. americanum densities but did not suppress P. penetrans. Brassica juncea ‘Pacific Gold’, B. napus ‘Dwarf Essex’, but not mustard ‘Caliente’ resulted in low densities of X. americanum in soil. Velvetbean and sesame increased both P. penetrans and X. americanum population densities. A moderately suitable host plant such as buckwheat was ineffective in managing both P. penetrans and X. americanum populations. These results emphasize that suppressive effects of any given cover crop are nematode-specific, and that within a group of cover crops, e.g., the Brassicaceae, species and varieties can vary substantially in their effectiveness. If the rotation crop chosen is a good host for the nematodes present, it may exacerbate the problem instead of controlling it.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".