On the determination of mass transfer in a concentration boundary layer
Bibliographic record
Abstract
The mass transfer of scalar quantities (e.g., O2 and nutrients) in aquatic environments is an important and complex process involving diffusion and advection. In a flowing environment, concentration boundary layers (CBL) occur above the surfaces of organisms when they are a sink or source of scalars. In this study, we used an O2 microsensor to profile the O2 concentrations in the CBL above photosynthesizing freshwater macrophyte (Vallisneria americana) leaves that were oriented parallel to the flow in a recirculating flow chamber at 0.5 and 3.3 cm s−1. Measured O2 profiles were nonlinear indicating the effect of higher order processes near the surface. O2 flux (Jobs) was estimated from these profiles by two nonlinear techniques, hyperbolic tangent and logarithmic models, and the commonly applied linear model. An integrated measurement of O2 flux (Jint) for each leaf was also measured independently in a stirred chamber. Whereas Jobs determined from the hyperbolic tangent (0.42 ± 0.04 [mean SE] µmol m−2 s−1) and linear (0.31 ± 0.04 µmol m−2 s−1) models overestimated and underestimated Jint (0.37 ± 0.05 µmol m−2 s−1), respectively, and were not velocity dependant, the hyperbolic tangent model provided the best fit (r2 = 0.88) compared with the linear model (r2 = 0.77). In addition, the slope of the regression against Jint (1.08 ± 0.06) was closest to 1.00 (i.e., a “perfect” fit). The logarithmic model varied with velocity and overestimated Jobs (0.98 ± 0.22 µmol m−2 s−1 at 0.005 m s−1 and 0.90 ± 0.18 µmol m−2 s−1 at 0.033 m s−1). These results were confirmed in an analysis of 21 published O2 concentration profiles measured next to sediments, microbial biofilms, planktonic algae, and epilithic algae. We would, therefore, recommend the hyperbolic tangent model to estimate mass transfer in a CBL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".