Bibliographic record
Abstract
The weed flora in the mulberry fields were investigated in Suwon, Jeonju, and Buan in May, July, and September of 2014. The objectives of this study were to use the survey data for establishing weed control methods and to bring awareness of possible problematic weeds in the Korean mulberry fields. The survey was conducted in 53 regions, covering approximately 145,925 m2. Altogether 153 weed species of 37 families were identified, of which 68 were annual, 39 species were biennial and 46 were perennial. The dominance was the highest with Digitaria ciliaris followed by Erigeron annuus, Chenopodium album, Echinochloa crus-galli var. crus-galli, Acalypha australis, Commelina communis etc. Exotic weeds presented 44 species with 28.8% of a total presence, of which Erigeron annuus was the highest, followed by Chenopodium album, Phytolacca americana, Conyza canadensis, Oxalis corymbosa etc. Especially, we should aware Senecio vulgaris, not controlled with glufosinate ammonium SL in the Korean mulberry fields because it was known as atrazine resistance in US, Canada, Germany etc. In the PCA plot, weeds presented in the mulberry fields were divided into two groups, Eclipta prostrata community and Stellaria aquatic community and weed flora of Suwon and Buan were different due to those only presented in Suwon.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".