Bioenergy production data from anaerobic digestion of thermally hydrolyzed organic fraction of municipal solid waste
Bibliographic record
Abstract
The presented dataset in this data article provides quantitative data on the production of bioenergy (biogas and biomethane) from mesophilic batch anaerobic digestion (AD) of thermally hydrolyzed organic fraction of municipal solid waste (OFMSW). The discussion and interpretation of the data are provided in another publication entitled “Hydrothermal Pretreatment of Source Separated Organics for Enhanced Solubilization and Biomethane Recovery” (Razavi et al., 2019). The data and information presented in the current data article include (1) the ratio of soluble to particulate chemical oxygen demand (COD) under different thermal hydrolysis condition, (2) the daily measured biogas and biomethane data, (3) the cumulative methane yield data in terms of mL CH4 produced per gram of volatile suspended solids (VSS) as well as feedstock added, (4) the ultimate methane yield data as well as the relative improvement in methane recovery compared to the control (non-hydrolyzed) digester, (5) the data of first-order organics biodegradation rate constants, (6) the procedure of measuring biogas composition via gas chromatography, (7) the procedure of converting the biogas/methane volume data acquired under the actual experimental condition (mesophilic temperature of 38 °C and atmospheric pressure) to the standard temperature (0 °C) and pressure (1 atm) condition, and (8) the procedure of determining the first-order kinetic rate constants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".