Enhanced Tomato Yield via Bumblebee Pollination: A Case Study in Durres, Albania
Bibliographic record
Abstract
This study investigated the impact of bumblebee (Bombus terrestris) pollination on the yield and quality of greenhouse-grown tomatoes.Control and experimental plots, each spanning 1 ha, were established within glass-enclosed greenhouses, in which tomatoes had previously been planted.The first plot had hives with insect pollinators Bombus terrestris from the beginning of flowering, while the other plot was not pollinated by bumblebees.After harvesting, the organoleptic properties of fruits were analysed for 3 clusters of 10 plants from each site and their weight ratio relative to the control.The production activity lasted about 70 days.As a result, it was determined that the fruits formed as a result of pollination with the participation of Bombus terrestris were more numerous and larger in size, and their weight was 25% higher (p0.05)than the fruit weight of the corresponding number of plants in the control group.Thus, the use of pollinating bumblebees in greenhouses opens up prospects for simplifying and reducing the cost of industrial cultivation of tomatoes in the closed ground, the implementation of this approach may increase the accessibility of the produce irrespective of the season and enhance the quality of the fruits, thereby potentially elevating their market value.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".