Surfactant-associated bacteria in the near-surface layer of the ocean from in-situ DNA sampling and SAR imaging
Bibliographic record
Abstract
Certain marine bacteria found in the near-surface layer of the ocean are expected to play an important role in the production and decay of surface active materials. Identifying a connection between marine bacteria and the production of natural surfactants may provide a better understanding of the global picture of biophysical processes at the boundary between the ocean and atmosphere, air-sea exchange of gases, and production of climate-active marine aerosols. Kurata et al. (2016) and Hamilton et al. (2015) have developed measurement methodology combining DNA sampling of sea surface microlayer with SAR satellite technology. Following Franklin et al. (2005) and Cunliffe et al. (2011), these authors used polycarbonate membrane filters in order to minimize potential contamination that may occur with other sampling techniques. A hydrophilic polycarbonate filter, attached to the sea surface by capillary forces, collected bacteria effectively from a 35-42 μm surface layer. A fly fishing technique was used in Kurata et al. (2016) and Hamilton et al. (2015) to ensure that the filter sat on the sea surface for a few seconds (away from the vessel and its wake in order to avoid these sources of disturbance to measurements of the microlayer). Samples from the water column at approximately 0.2 m depth were taken with a peristaltic pump for comparison with the sea surface results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".