Observations of the spatial structure of internal waves in a small mid-latitude lake
Bibliographic record
Abstract
Internal waves are features of stratified waters that vary in space and time. Traditionally, such features have been investigated using data accumulated by an array of self-recording sensors. There are typically one or more moorings located in a study area, with each mooring having sensors located at several different depths. While the temporal resolution of data logged by each sensor is excellent, the spatial resolution of data recorded by such a system of sensors is usually poor. Having a sensor move through the water column is one way of improving the spatial resolution of data, but such data tends to have poor temporal resolution at any given location. With the goal of obtaining data with adequate spatial and temporal resolution to characterise internal waves, a study was undertaken that combines the temporal resolution provided by a traditional moored sensor array, with the spatial resolution of data collected by sensors moving across the study area. This study was conducted at Loon Lake, which is a small (1.5 km long and 0.5 km wide), deep (/spl sim/50 m) lake located near Maple Ridge, British Columbia, Canada. Direct measurements of the temperature structure within the thermocline were made with moored thermistor chains, as well as with a thermistor mounted on an autonomous underwater vehicle, or AUV. Two thermistor chains, each with six thermistors within the thermocline, were moored along the major axis of the lake for six weeks. A SeaBird SBE19 CTD was mounted onboard the Underwater Research Lab's PURLII AUV. On three separate days PURLII followed a sawtooth pattern within the thermocline, and along the major axis of the lake. These data were compared and contrasted to provide a comprehensive description of the internal temperature field.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".