Bibliographic record
Abstract
In 2020, two experiments were established in the area of Pravlov, where the effect of different weed control measures (chemical or alternative) on weeds in a vineyard was tested. The aim of this diploma thesis was to evaluate their effectiveness and economic importance. The most important weeds infesting the vineyard were red root amaranth, forget-me-not, chickweed, Canadian horseweed, groundsel and field bindweed. The effectiveness of these weed control measures was evaluated using the estimation method. The following variants showed the highest effectiveness in all monitored weeds: ROUNDUP BIAKTIV, STOMP 400 SC (combination of these two herbicides also had a good effect), mechanical hoeing and the use of flaming (high temperature). The only rather problematic weed was field bindweed. A good effect on this weed was only achieved with ROUNDUP BIAKTIV in combination with STOMP 400 SC or STOMP 400 SC alone. ROUNDUP extra rychlý (Express) and 99% acetic acid in doses of above 20% also showed a very good effect, but from an economic point of view these are financially expensive variants.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".