Biology and control of common waterhemp (Amaranthus tuberculatus var. rudis)
Bibliographic record
Abstract
Sixteen field experiments were conducted over a three-year period (2003--2005) at two Ontario locations to evaluate efficacy of pre-emergence and post-emergence herbicides for the control of waterhemp in corn and soybeans. The waterhemp biotype was resistant to both the acetolactate synthase inhibitors and photosystem II inhibitors at one location and resistant to only the acetolactate synthase inhibitors at the second location. Waterhemp control was dependent on the resistance pattern of the waterhemp and the environmental conditions at each location. In corn, isoxaflutole, 's'-metolachlor/atrazine, mesotrione, or 'S'-metolachlor/atrazine plus mesotrione applied preemergence, and dicamba, dicamba/atrazine, and mesotrione plus atrazine applied postemergence all resulted in greater than 90% control of waterhemp. In soybeans, 's'-metolachlor plus metribuzin applied pre-emergence, and acifluorfen, fomesafen, imazamox plus fomesafen, and glyphosate applied post-emergence, provided greater than 87% waterhemp control in three out of four site-years. Two greenhouse experiments found triazine-resistant waterhemp produced less seed than triazine-susceptible waterhemp.
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".