CO <sub>2</sub> demand-supply balance in a composting-based closed-loop plant factories.
Bibliographic record
Abstract
Abstract Carbon dioxide enrichment is a technique employed in controlled environment agriculture (CEA) systems (i.e., vertical farms) to improve crop yield. However, the CO 2 for enrichment is sourced from fossil fuels, increasing the carbon footprint of these operations by adding CO 2 to the atmosphere. Sourcing CO 2 from biowaste instead can be a more sustainable option. Particularly, composting crop residues generated in CEA systems can generate CO 2 while valorizing biowaste. This study assesses the possibility of meeting the CO 2 demand for enrichment in a CEA system by composting residues of baby lettuce - the most common crop grown in CEA systems. Using theoretical modelling, we estimated the CO 2 required to grow baby lettuce in a hypothetical CEA system at an enriched CO 2 concentration of 1000 ppm. The CO 2 derivable by composting lettuce residues generated by a CEA system with two different types of hydroponic system (mat- and plug-based) was determined through aerobic incubation and CO 2 respirometry. Depending on the hydroponic system, composting crop residues generated in the hypothetical CEA system could produce around 1.4-6% of the total CO 2 required for enrichment. Comparing the estimated demand versus supply of CO 2 on a mass basis showed that composting crop residues alone is likely insufficient to meet the enrichment demands of a CEA system growing baby lettuce at 1000 ppm. Additional biomass (e.g., source separated urban biowaste) might be required to meet the CO 2 demand for enrichment solely through composting.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".