Bibliographic record
Abstract
Chen, Y., Shen, X. and Fang, Y. 2013. Fenclorim effects on rice germination and yield. Can. J. Plant Sci. 93: 237–241. Weedy rice (Oryza sativa f. spontanea) is a serious problem in rice-producing areas. The objective of this study was to evaluate the effect of the safener fenclorim on rice seed germination and yield when used in conjunction with the pre-emergent herbicide pretilachlor in the growth chamber and in the field. Rice seed germination was accelerated by soaking seeds in fenclorim (0.67 g L−1), and pretilachlor (0, 450, 900, 1800, 3600 and 7200 g a.i. ha−1) was applied 1, 3, 6, 12, 24, and 48 h after sowing in a growth chamber. Seeds were also soaked in fenclorim, and then pretilachlor was sprayed (0, 450 and 900 g a.i. ha−1) 1 h after sowing in the field. The percentage of seedling germination for cultured rice was significantly increased by soaking in fenclorim prior to application of the pre-emergence herbicide pretilachlor compared with the control in the growth chamber. The application of the safener fenclorim increased rice yield by 56% and 50% in treatments with 450 and 900 g a.i. hm−2 pre-emergence pretilachlor, respectively, and the weed population, height, and dry matter production were significantly reduced by pretilachlor application.
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".