Results from the multi-species Benchmark Problem (BM3) using one-dimensional models
Bibliographic record
Abstract
The IWA's Biofilm Modeling Task Group created a multi-species benchmark problem in which heterotrophic bacteria, nitrifying bacteria, and inert biomass coexist in a biofilm. Members of the Task Group submitted solutions from nine different one-dimensional models. The most important distinctions among the models were (1) whether the model required a full numerical solution or was solved with a spreadsheet, and (2) the way the biomass types were distributed in the biofilm. The models that protected the slow-growing species by having them accumulate away from the outer surface always had the largest surface coverage by nitrifiers and inerts, but the heterotroph coverage declined to compensate. Coverage by heterotrophs and removal of substrate COD were most strongly affected by dilution from nitrifiers and inerts near the outer surface. Models that did not allow the nitrifiers and inerts to dilute the heterotrophs significantly in the outer layer predicted more removal of COD than did the other models. The choice of the model to use depends on the user's needs and the relative importance of including protection of slow-growing species and/or dilution of fast-growing species.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".