Partnering Commercial Greenhouses with Dairy Manure Based Anaerobic Digestion Systems - Quantifying Energy Synergies
Bibliographic record
Abstract
One way to improve the economics of dairy manure-based anaerobic digestion systems (ADS), controlled environment agriculture systems, and overall sustainability for both dairy and greenhouse enterprises is to share surplus electricity and heat produced by the farm-based digesters with greenhouses. A three-year project we recently completed had an overall goal of quantifying the synergies of surplus heat and electricity produced by manure-based anaerobic digesters and the electrical and heat demands of commercial greenhouses. As a part of the project, on-site data was collected over its duration from three commercial dairy farms with operating anaerobic digesters (two in NY and one in ME) and from two smaller commercial greenhouses (NY and Ontario, Canada). Collected data, along with other available data and engineering principles, were used to develop and validate computer models with a purpose of predicting surplus heat and electricity from ADS and the associated demands of commercial greenhouses. The computer models were then developed into a user-friendly software package that we refer to as “Cornell Digester Greenhouse Simulation Software” (CDGSS).
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".