Overview of the Use of Sustainable Energies in Agricultural Greenhouses
Bibliographic record
Abstract
Global concern on environmental problems like climate changes has altered our energy patterns promoting non-polluting renewable energies instead of fossil fuels. Technological advances in sustainable energy technologies allow their increasing use in all sectors of everyday life. Agricultural greenhouses utilize energy for heating, cooling and operation of various electric devices. The highest amount of energy used in greenhouses is consumed in heating them. Controlling crops growth conditions including temperature results in higher productivity and in better economic results. Various sustainable energies including renewable energies and high efficiency and low carbon energy technologies have been used in commercial scale and the technical and economic viability of others has been investigated in experimental scale. Among renewable energies solar energy, biomass and geothermal energy can be used in order to cover part or all of the energy requirements for heating, cooling and power generation of greenhouses. Energy efficient and low carbon technologies like co-generation of heat and power, heat pumps, fuel cells but also waste heat can be used also for energy generation in them. Governmental energy incentives for the promotion of sustainable energies like feed-in tariffs or net-metering allow the use of the abovementioned energy technologies for electricity generation in greenhouses offering additional economic benefits to the farmers. Use of the sustainable energies which are mature, reliable and cost effective in greenhouses results in mitigation of climate changes, use of local renewable energy resources instead of fossil fuels and better profitability of the cultivated crop.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".