Bibliographic record
Abstract
Energy is one of the largest components of the production cost in Agricultural activities. The efficiency of its use will often be compromised in favour of other equally important factors. Data were collected in 10 cassava farms by using face to face questionnaire method to determine the energy input in cassava production. Mathematical expressions were used to evaluate the energy requirement for each of the defined unit operations. Energy requirement in land preparation, planting, crop maintenance (fertilization and weed control) and harvesting were determined. It was observed that 78.67% of the total energy input used in cassava production was indirect, while 21.33% was direct. The average energy input in the production of cassava was 8571.26 MJ/ha, while 9960.00 kg was the average yield obtainable per hectare. Energy input in fertilizer was the highest with 64.0% of the total energy input; followed by diesel fuel with 19.50%. The net energy and energy productivity value were estimated to be 46,655.77 MJ/ha and 1.18 MJ/kg respectively. The ratio of energy output to energy input was 7.1.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".