Modeling Mushrooms’ Carbon Dioxide Emission and Heat Exchange Rates for Synergistic Cultivation with Leafy Greens
Bibliographic record
Abstract
The cultivation of mushrooms in controlled environments generates a significant amount of CO2 as a by-product. This presents opportunities for carbon dioxide (CO2) enrichment in leafy green production. This study aimed to develop a model for CO2 emission and heat exchange rates that can be used to support the synergistic cultivation of mushrooms and leafy greens. This was achieved by aggregating data from literature with experimental data gathered in two different testing spaces. The average CO2 emission and heat exchange rates for shiitake incubated at 21 °C were determined and a CO2 emission rate model for mixed substrate in incubation was developed based on indoor temperature variations. The results indicated that oyster mushrooms have a notable CO2 enrichment potential, twice that of shiitake in the incubation stage and five times more in fructification. Additionally, oyster mushrooms released a significant amount of heat during incubation. In contrast, shiitake mushrooms with their minimal heat exchange rate during incubation could offer an energy-efficient option for synergistic cultivation with leafy greens in environments where cooling is required year-round. Moreover, it was observed that the CO2 emission rate of a full-scale incubation chamber is strongly correlated with indoor temperature. These findings offer valuable information for modeling the CO2 emission and heat exchange rates of mushroom and leafy green farms.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".