Regulation of the composition of plantations by the method of injection of herbicides into unwanted tree species: history and perspectives
Bibliographic record
Abstract
This literature review is devoted to the evaluation of the method of injection of herbicides into tree trunks of unwanted tree species and the problem of its improvement.The applied traditional methods of silvicultural care, based on the mechanical removal of unwanted woody plants, are very laborious and ineffective.A good alternative to mechanical care is the chemical method, which, along with spraying unwanted vegetation, also injects chemicals (in particular herbicides) into the trunks of deciduous trees.The injection method can be used at different stages of forest cultivation and allows solving a whole range of silvicultural problems -from promoting the natural regeneration of conifers and caring for natural and artificial spruce stands to preventing the vegetative regeneration of aspen and other unwanted tree species after final felling.In addition, this method is most consistent with modern environmental safety requirements for the chemical method of regulating the composition of vegetation.This method, in addition to Russia, is widely used in many foreign countries -the USA, Canada, Great Britain, Australia.Based on the analysis of literature data and the results of the authors' research, a conclusion is made about the prospects for further application of the herbicide injection method and the need to improve it, taking into account changes in the range of drugs approved for use on the territory of the Russian Federation.Tornado (water solution (WS), 360 g/l glyphosate) and аrbonal (water soluble concentrate (WC), 250 g/l im azapyr), as well as their mixtures, should be considered the most promising drugs for use in forestry.Регулирование состава и густоты насаждений способом инъекции гербицидов...
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".