Bubble biofilm: Bacterial colonization of air-air interface
Bibliographic record
Abstract
Microbial mats or biofilms are known to colonize a wide range of substrates in aquatic environments. These dense benthic communities efficiently recycle nutrients and often exhibit high tolerance to environmental stressors, characteristics that enable them to inhabit harsh ecological niches. In some special cases, floating biofilms form at the air-water interface residing on top of a hydrophobic microlayer. Here, we describe biofilms that reside at the air-air interface by forming gas bubbles (bubble biofilms) in the former Ytterby mine, Sweden. The bubbles are built by micrometer thick membrane-like biofilm that holds enough water to sustain microbial activity. Molecular identification shows that the biofilm communities are dominated by the neuston bacterium Nevskia. Gas bubbles contain mostly air with a slightly elevated concentration of carbon dioxide. Biofilm formation and development was monitored in situ using a time-lapse camera over one year, taking one image every second hour. The bubbles were stable over long periods of time (weeks, even months) and gas build-up occurred in pulses as if the bedrock suddenly exhaled. The result was however not a passive inflation of a dying biofilm becoming more fragile with time (as a result of overstretching of the organic material). To the contrary, microbial growth lead to a more robust, hydrophobic bubble biofilm that kept the bubbles inflated for extended periods (several weeks, and in some cases even months).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".