Plant Production in Solar Collector Greenhouses - Influence on Yield, Energy Use Efficiency and Reduction in CO2 Emissions
Bibliographic record
Abstract
A semi-closed solar collector greenhouse was tested to evaluate the yield and the energy saving potential compared with a commercial greenhouse. As such, new algorithm for ventilation, carbon dioxide (CO2) enrichment, as well as for cooling and heating purposes initiated by a heat pump, cooling fins under the roof and a low temperature storage tank were developed. This cooling system showed that the collector greenhouse can be kept longer in the closed operation mode than a commercial one resulting in high levels of CO2 concentrations, relative humidity and temperatures. Based on these conditions, the photosynthesis and associated CO2 fixations within the plant population were promoted during the experiment, resulting in a yield increase by 32%. These results were realized, although the mean light interception by energy screens and finned tube heat exchangers was increased by 11% compared to the reference greenhouse. The energy use efficiency was improved by 103% when the collector greenhouse was considered as energy production facility. In this context, the energy saving per kilogram produced tomatoes in the collector greenhouse is equivalent to the combustion of high amounts of different fossil fuels, where the reduced CO2 emissions ranged between 2.32 kg and 4.18 kg CO2 per kg produced tomatoes. The generated total heat was composed of approximately one-third of the latent heat and over two-thirds of the sensible heat, where a maximum collector efficiency factor of 0.7 was achieved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".