Characterization of adsorbed microlayer thickness on an oceanic glass plate sampler
Bibliographic record
Abstract
The thickness of solution layers adsorbed onto rotating glass plates designed for use on an oceanic glass plate sampler was investigated in laboratory experiments using optical techniques. Using the Beer‐Lambert Law, light attenuation measurements were used to calculate the thickness of adsorbed solution layers on a rotating glass disk with and without salt and surfactants. The observations have shown that the adsorbed film thickness can vary between 80 and 40 µm for glass rotation speeds between 4 and 16 cm s−1, depending on salinity and surfactant concentrations. For example, the thickness of a film of water with 40 ppt of salt and 5 cm s−1 rotation speed was in the range of 80 µm. The adsorbed layer thickness increases with increasing salt concentration and with increasing concentrations of surface active substances. These results are comparable to results obtained by vertically dipping a glass plate and determining film thickness from the collected volume of water. Because the glass disk rotational speed significantly influences the thickness of the adsorbed solution layer, it is important that the speed is maintained at a value for which the adsorbed thickness has been calibrated. At typical oceanic salinities, the dependence of the adsorbed film thickness on rotation speed was limited. However, even small changes in surface active substances resulted in significant thickness changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".