Surface layer dynamics and a study of finestructure in Knight Inlet, British Columbia
Bibliographic record
Abstract
In the first part of this thesis I investigated the surface layer dynamics of Knight Inlet to see which of the governing forces: inertial accelerations, pressure gradient, or stress, dominated the momentum balance for a steady two-layer flow. I estimated the inertial terms and pressure term in the momentum equation, which had been integrated over the surface layer, from conductivity, temperature, and depth data measured in Knight Inlet in the springs of 1986 and 1987. I solved for the coefficient of interfacial friction, k, so that an estimate of the interfacial stress, Tί = pkΔu², could be made. I obtained the idea of the Knight Inlet analysis from an earlier attempt I had made at resolving the balance of forces in the Fraser River plume. I found an estimate of the friction coefficient at the interface for the plume; k = 1.55 x 10¯⁴, which was much smaller than an assumed value used by Cordes, et al. (1980). The results of the Knight Inlet study showed that within the inlet (inside the sill), the pressure gradient, and the stress dominated the balance for high runoff conditions. Estimates of the coefficient of friction were on the order of 10⁻² and 10⁻³. The depth of the interface appeared to be constant inland of the sill. The second part of this thesis was a qualitative study of fine structure in Knight Inlet. I processed the CTD data measured in 1986 and 1987 to find the first differences of temperature and salinity. I used the profiles of ΔT and ΔS to describe features present in the upper and lower water. Later, data that had been measured with a special microprofiler at the same time as the 1987 CTD data were sampled, were compared to the CTD data. For this comparison, I studied profiles of [formula omitted] (calculated as a centered first difference), and the log of the variance of [formula omitted]. Results of this study were that the upper water (< 30 m) appeared more highly active than the deeper water (> 30 m), especially at the head of the inlet, at the sill, and in the region of the interface. The deeper water contained fluctuations of temperature and salinity that were concentrated in patches which were several meters thick. For the year, 1987, the microprofiler revealed the existence of temperature variations that were more significant than I had originally judged from the profiles of ΔT and ΔS. Values of the variance of the temperature gradient, [formula omitted] in some areas of the deeper water were almost as large as values in the upper water.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".