Bibliographic record
Abstract
acres planted to potatoes, representing 43 % of U.S. acreage. Fifty-four percent of U.S. production of potatoes is located in these three states: 25.6 billion pounds per year (42,000lbs/A). The value of potato production in the three states is $1.3 billion per year, ($2,100/A) [8]. Weed Species Weeds compete with potatoes for light, water, and nutrients and interfere with harvest operations. Dense weed infestations restrict the growth of potato plants, which results in undersized tubers. Potatoes are slow to emerge, offering little competition to weeds from the time of planting until the leaves fill the row [23]. All fields in which potatoes are grown contain billions of weed seeds [14]. The most common and troublesome weed species in Northwest potato production include annual grasses (barnyardgrass, foxtails), annual broadleaves (nightshade, lambsquarters, pigweeds, kochia) and perennials (quackgrass, Canada thistle) [1]. Table 19.1 shows estimates of the percent acreage of Pacific Northwest potatoes infested with specific weed species and the potential potato yield loss if the individual species are not controlled. As can be seen, most fields (90%) are infested with the three common broadleaf species (lambsquarters, pigweeds and nightshades) and with the two most common grass weed species (foxtails, barnyardgrass).
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) | 0.018 | 0.007 |
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".