Technological transfer of agrobiology research results in the national economy of the Republic of Moldova
Bibliographic record
Abstract
At the beginning of 2024, the Academy of Sciences of Moldova began hearings on reports on the results of scientific research in the field of agriculture, biology and food security for the period 2020-2023. Major importance was given to the results of the technological transfer, achieved during this period, which includes: the implementation of the 'Soil Register of the Republic of Moldova' and the 'Program of land improvement in order to ensure the sustainable management of soil resources for the years 2021-2025'. An important leap was made in the wine complex by increasing the commodity production of table grapes to about 100 thousand tons, of which 80 thousand tons for export to the EU states, Ukraine, Belarus and the Russian Federation. Projects have been carried out in the technology of grape processing and the production of competitive wines on the world market, including the EU, China, the USA, Canada, and the UK. A new technology was implemented to produce highquality food vinegar from apple and grape concentrates, spiced and sherry. A new strain of acetic bacteria was used at the base of the production line. Thanks to the high result of the selection of fish from the surface lakes 'Crap de Mândâc' and 'Crap de Telenesti', ensuring 100% the market of the Republic of Moldova with fry and fresh fish. Animal science has achieved the first technological transfer of new breeds and lines of goats, sheep and milking cows 'Bălțata', ensuring 100% of the branch with high quality semen. The 'Selecția' Institute secured 70% of the seed market of the soybean culture on the entire area of 50 thousand ha annually.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".