Acetylene blockage technique as a tool to determine denitrification potential of a biomass fixed on an organic media treating wastewater
Bibliographic record
Abstract
Biofiltration on organic media is used in many wastewater applications. However, no tools are available to survey the microorganisms' activity inside the process. The acetylene blockage technique was adapted to evaluate the potential denitrifiers' activity. The objectives of this work were to adapt and verify the applicability of the method by evaluating its sensitivity and variability. A closed cell was built and installed in an open network where pure gaseous nitrogen and acetylene had been successively injected at fixed rate on filtering media samples. N2O produced by the denitrifying biomass was monitored during the test. Assays were realized in replicates, with different quantities of filtering media, to observe the sensitivity and the variability of the method. Potential denitrifying activity and quantity of biomass correlation shows that the adapted method was sensitive for 50 to 150 g (wet basis) of filtering media. Moreover, when under an acetylene atmosphere, the coefficients of variation observed for the tests varied between 10% and 32% when under anoxic conditions. This work shows that the technique as adapted could be used as a tool to survey the potential activity of denitrifiers fixed on an organic media.Key words: denitrification, biofilter, acetylene, nitrous oxide, organic media, potential activity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".