PRE and POST Herbicides for Management of Goldenrods (<i>Solidago</i>spp.) and Black Bulrush (<i>Scirpus atrovirens</i>) in Wild Blueberry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Field studies were conducted in wild blueberry in 2007 and 2008 to evaluate the efficacy of hexazinone applied PRE and multiple application timings of POST mesotrione on goldenrods and the efficacy of burning, terbacil applied PRE, nicosulfuron/rimsulfuron applied POST, and multiple application timings of mesotrione applied POST on black bulrush. Mesotrione application timings were at 10 and 30 cm of height, floral bud initiation, and full flower. Hexazinone applied at 1.92 kg ha −1 in 200 L water ha −1 effectively suppressed goldenrods. At least 90% goldenrod damage was achieved with mesotrione POST applied at 101 g ha −1 in 300 L water ha −1 before full flower, following hexazinone PRE at two of three sites. Damage following mesotrione was more variable when hexazinone was not applied. Mesotrione efficacy was lower when applied in the crop year, but a crop-year registration may be warranted to improve harvest ease and increase berry quality. A single application of mesotrione at the label rate did not adequately control black bulrush. Ninety percent black bulrush control was achieved with rimsulfuron/nicosulfuron applied at a rate of 0.03 g L −1 of water with 0.2% v/v nonionic surfactant. Equivalent levels of control were achieved with sequential mesotrione applications at the label rate.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it