Bibliographic record
Abstract
Abstract There is a necessity to deeply transform the agriculture sector. Farmers earn low income from agriculture and the irregular weather turn their crops to dust. Hence there is an impulse to bring agriculture revolution for sustainable development. On searching on the solutions, an industrial hemp- Cannabis sativa, a low cost annual crop might be considered as a trillion dollar super crop. Hemp was our first agricultural crop, and remained the planet's largest crop. Its cultivation not requires chemicals, pesticides and can be grown in rotation with other crops. Hemp farming is completely sustainable. Today hemp is being cultivated mostly by China, Hungary, England, Canada, Australia, France, Italy, Spain, Holland, Germany, Poland, Romania, Russia, Ukraine, India and throughout Asia. There is currently renewed interest in once again growing this versatile crop. While hemp faces significant legal obstacles due to its close relationship to the marijuana plant, there are a number of states, are moving toward reviving the hemp industry. This review paper is intended to highlight the core of highbrow commercial plant, the industrial hemp and its impact on ecosystem. In addition to that it will provide a platform to focus more to cultivatesucharesourcefulplant. Key: agriculture, revolution, hemp, commercial, sustainable.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".