Bacterial community composition in marine bioaerosols
Bibliographic record
Abstract
Dynamics and impact of marine bioaerosols are still poorly understood. The sojourn time of airborne microorganisms can last to several days, so even transcontinental transport is likely to occur. Only little is known about bacterial communities of marine bioaerosol in terms of diversity community composition. Few recent studies showed that bioaerosols often exhibited a similar bacterial community as the subjacent ecosystems. We combined two different sampling strategies in our recent investigation. In order to elucidate the spatial variation, 36 samples were gathered during a ship cruise from the North Sea to the Baltic Sea in August 2011 using an impingement sampler (XMX/2L-MIL, Dycor, Canada). Furthermore, a one year survey is conducted to investigate temporal variation of bioaerosols at the offshore island Helgoland (German Bight, North Sea). The s amples were analysed with culture independent molecular methods. Quantification was carried out using q-PCR and C-FLAPS analysis. The community structure was analysed with ARISA-fingerprints. Phylogenetic analysis was performed via 454 16S tag-sequencing. \n \nFirst results showed high variation in spatial distributions, concerning the concentration of airborne bacteria, ranging from 10² to 105 cells per m-³. This is probably explained by the origin of sampled air parcels, correlated to the calculated backward trajectories, which also showed high variation. These results will be integrated within the context of the 454 sequencing data. Furthermore, results of the temporal aspect will be shown for the first time.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".