Energy use pattern and optimization of energy consumption for greenhouse cucumber production in Iran using data envelopment analysis (DEA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study a non-parametric method of Data Envelopment Analysis (DEA) is used to estimate the energy efficiencies of cucumber producers based on eight energy inputs including human power, diesel fuel, machinery, fertilizers, chemicals, water for irrigation, electricity and seed energy and single output of production yield. Data were collected using face-to-face surveys from 25 greenhouses in Khuzestan province of Iran. Energy indices, technical, pure technical and scale efficiencies were calculated by using Data Envelopment Analysis (DEA) approach for 25 cucumber greenhouses. Total energy input and output were calculated as 163994 MJha-1 and 62496 MJha-1, respectively, whereas diesel fuel consumption with 45.15% was the highest level between energy inputs. Energy output-input ratio, energy productivity and net energy gain were 0.38, 0.47 kg MJ?1, -101498 MJ ha?1, respectively. The average values of TE, PTE and SE were 88%, 91% and 96%, respectively.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it