Main Trends Of Increase In Productivity Of Beekeeping
Bibliographic record
Abstract
A big number of entomophilous plants are cultivated in Russia. These plants need a cross-pollination which is effected by bees only. Beekeeping is a quite specific branch and in a lot of countries it is automated and mechanized. For estimation of the level of labour productivity as well as the level of mechanization and automation in the branch, it is proposed to implement a ROL index. In our work the estimation of ROL across the countries — leaders in bee-keeping — was done. It is proposed to implement the expenditure index which is inverse to the profitability. The objective function of ROL optimization is proposed. The objective function considers the level of mechanization and automation of the output. Optimization of objective function parameters was carried out based on example of Krasnodar region for 4 threshold values of ROL: $ 10 000 per person, $ 20 000 per person, $ 30 000 per person, $ 40 000 per person. These are the main levels of automation and mechanization of technological processes in beekeeping. It was stated that it is possible to increase the ROL up to the advanced countries level by means of increase of automation level. In this case the service norm also increases up to values of USA and Canada. The search of optimal solution of objective function and optimal values of its parameters gave the possibility to establish the rational value of bee-families service norm related to a beekeeper for all levels of automation and mechanization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".