Microsensor measurement of oxygen concentration in biofilms: from one dimension to three dimensions
Bibliographic record
Abstract
In this study, we measured oxygen concentration in biofilms in one dimension in field conditions and in three dimensions in laboratory conditions by using a robust oxygen microsensor in combination with an automation and data acquisition system. The biofilms were on the discs of rotating biological contactors treating domestic wastewater. The results of this study provide experimental evidence on oxygen distribution in wastewater biofilms and on biofilm structure. (1) The three dimensional measurements of oxygen concentration in biofilms revealed "pockets" of oxygen in deep sections of biofilms. In these isolated "pockets," located 600-760 microm from the biofilm surface, dissolved oxygen concentration was as high as 1 mg/L. This depth of oxygen diffusion is deeper than what was determined based on one dimensional measurements. (2) The heterogeneity of oxygen distribution was related to the surface structure of biofilms. The structure of the biofilm surface affected the diffusion boundary layer over the surface and, in turn, the oxygen diffusion and distribution inside biofilms. (3) Oxygen concentration in biofilms changed generally from a high degree of heterogeneity near the biofilm surface to a low degree of heterogeneity in deep sections of biofilms, indicating a cell-clusters-like structure near the surface and a more compact base layer close to the substratum.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".