Upwelling flow dynamics in long canyons at low Rossby number
Bibliographic record
Abstract
Submarine canyons, topographic features incising the continental slope, vary in both shape and size. The dynamics of short canyons have been observed and described in the field, in the laboratory, and with numerical simulations. Flow within long canyons, such as Juan de Fuca canyon, located between Vancouver Island and Washington State in the Pacific Northwest, is less well understood. Physical models of both long and short canyons have been constructed to understand the upwelling dynamics in long canyons and how upwelling changes, as compared with the dynamics of short canyons, at low Rossby number. Stratification and rotation, both important parameters in determining the dynamics in canyons, can be controlled and scaled accordingly for replication of oceanic conditions. The physical model is spun up to an initial rotation rate, and the flow is forced by increasing the rotation rate over the equivalent of several days. Flow visualization is used to determine the strength and location of upwelling, the strength and mechanisms generating vorticity, as well as the differences between the flow within the long and short canyons. The pattern of upwelling between the two canyons is significantly different in the horizontal with upwelling occurring through the canyon head in the short canyon and upwelling occurring close to the mouth along the downstream rim in the long canyon. At high Rossby number, upwelling is similar in both the long and short canyon and is driven by advection. However, as Rossby number decreases, the flow in the long canyon is more strongly affected by the strong convergence of the isobaths near the canyon than by advection alone.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".