Bibliographic record
Abstract
Anaerobic digesters decompose organic matter biologically in the absence of oxygen. In some cases, in addition to waste management, the purpose of anaerobic digestion (AD) is to produce methane, which can be used for energy. In the Fraser Valley region, potentially 30 MW of energy can be generated through AD with the additional benefits of reduced odour, green house gas (GHG) emissions and soil and water contamination, which is produced currently from artificial fertilizers. The main goal of this research project is to develop an anaerobic digestion calculator that would assist farm and herd owners in the Lower Fraser Valley in making decisions on choosing suitable anaerobic digestion technologies for their own farms. The calculator is developed from Excel spreadsheets and graphical user interfaces (GUIs). These user interfaces take inputs, send the inputs to the corresponding spreadsheet cells, and block invalid inputs from causing calculation error. The new calculator uses the Lawrence and McCarty kinetic model to calculate substrate consumed during AD. This calculator takes hydraulic retention time (HRT) and feed, via animal counts, single-defined flow or mixing several waste sources, as inputs. From these inputs and default kinetic parameters, which can be modified, reactor size, biogas production rate, effluent characteristics, capital cost and revenue of the AD plant are calculated and summarized for users. Users can select one of the three possible digester configurations: completely-mixed, plug-flow and mixed plug-flow and heat and electricity co-generation or biogas upgrading. Currently the calculator is valid for simulating AD in the mesophilic temperature range only. Further modifications are needed to include other kinetic models, input more feed types and simulate thermophilic AD.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".