A Comprehensive Review on Sustainable Industrial Vertical Farming Using Film Farming Technology
Bibliographic record
Abstract
Rapid population growth is expected to lead to the global population reaching 8.9 billion by 2050. In order to sustain such population growth, global food production must grow more than 70% by 2050. Arable land per capita, however, is on the decline. Vertical farming (VF) provides an enterprising solution to these concerns. VF utilizes stacked levels of growing racks and beds to maximize grow space per square foot of land and typically uses hydroponics to reduce water use. Similarly, film farming (FF) is a new agricultural technique developed in Japan for the soilless cultivation of crops while drastically reducing water use. FF has the potential to be integrated into VF systems to improve water use efficiency, and further improve food safety. This, however, relies on the possible improvements in yield and plant quality to increase sales volume and price to offset the added cost of FF. This review illustrates a cost-benefit analysis of a theoretical VF to show the yield increase and price point needed for FF integration to be viable as 27 247 kg (43.57%) and $9.67/kg (26.90%) respectively. This review also shows the benefits to yield and quality is enough for the integration to be viable.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".