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Record W4389551986 · doi:10.3390/su152416740

Modeling Mushrooms’ Carbon Dioxide Emission and Heat Exchange Rates for Synergistic Cultivation with Leafy Greens

2023· article· en· W4389551986 on OpenAlexafffund
Marc-Antoine Meilleur, Diane Bastien, Danielle Monfet

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIncubationMushroomCarbon dioxideLeafyChemistryOysterHorticultureBotanyFood scienceEnvironmental sciencePulp and paper industryBiologyEcologyBiochemistryEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.323
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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