Removing debris from soil, separating worm eggs, and counting them
Bibliographic record
Abstract
Many nematodes (microscopic roundworms) are soil-dwelling plant parasites that infect the roots of plants and cause billions of dollars of crop loss worldwide on an annual basis. Heterodera glycines, the soybean cyst nematode (SCN), is the most damaging soybean pathogen in the United States and Canada, causing hundreds of millions of dollars in crop loss annually. The SCN infects the roots of soybeans and siphons nutrients from the plants, leading to stunted growth and reduced crop yields. The amount of damage and yield loss caused by the nematode is related to several factors, including the number of eggs (egg population density) in the soil. Knowing the SCN population density in the soil can be useful in guiding the use of management strategies and assessing the success of management efforts in SCN-infested fields. The objectives of this work were: 1) to develop a method of purifying eggs from suspension with debris and 2) to develop new methods to automate counting of eggs once extracted from cysts and purified. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223386
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".