Estimation of the efficiency of the herbicide ‘Axial Cross’ for grain crops
Bibliographic record
Abstract
The current paper has presented the results of a two-year study (2018–2019), the purpose of which was to study the efficiency of the herbicide ‘Axial Cross, EC’ (45 g/l of pinoxaden + 5 g/l of florasulam + 11.25 g/l of antidote cloquintoset-mexil) in 6 regions of the Russian Federation, namely on the crops of winter wheat variety ‘Ershovskaya’ in the Volgograd region, on the crops of winter wheat varieties ‘Skipetr’ (2018) and ‘Snigurka’ (2019) in the Voronezh region, on the crops of spring wheat variety ‘Altaiskaya Zhnitsa’ in the Altai Territory, on the crops of spring wheat variety ‘Uralosibirskaya’ in the Omsk Region, on the crops of spring barley variety ‘Acha’ in the Sverdlovsk Region, on the crops of winter barley variety ‘Rubezh’ in the Krasnodar Territory. The trials were laid on plots of 25 m2 in four sequences. The weed infestation of crops was estimated by a quantitative method before the treatment, in 30 and 5 days after it and before harvesting. The efficiency of the herbicide was calculated in relation to the untreated control. There has been established that the use of ‘Axial Cross, EC’ at application rates of 0.7–0.9–1.1 l/ha has provided a high degree of suppression of both cereals and dicotyledonous weeds. At the maximum rate of application, there was a 100 % efficiency of the herbicide against yellow-foxtail grass, wild oats, green-foxtail grass, blind weed, canker rose and black bindweed, 97.4 % against loose silky bent, 95.6 % against airif (catchweed). There was up to 68.1–77.7 % efficiency against Canadian thistle, corn sow thistle and field bindweed. The greatest yield increase after the use of the herbicide ‘Axial Cross, EC’ was obtained from the winter wheat varieties ‘Skipetr’ (0.88 t/ha) and ‘Snigurka’ (1.65 t/ha) in the Voronezh region.
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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".