Thermohaline staircases in the Arctic Ocean: Detection, evolution, and interaction
Bibliographic record
Abstract
Thermohaline staircases consist of a series of horizontal, well-mixed layers, each on the order of a meter thick, separated by thin interfaces, across which temperature and salinity make abrupt jumps. While they have been consistently observed several hundred meters below the surface of the Arctic Ocean for over fifty years, little is known about their long-term evolution. Such stratification structures affect the propagation of internal waves and, because of an effect called internal gravity wave tunnelling, interactions between internal waves and staircases can be complex. This thesis presents a novel method of detecting thermohaline staircase layers in observations, analyzes their evolution on a decadal scale, and examines their interactions with internal waves. Inspired by the patterns made by observations of thermohaline staircases in temperature-salinity space, I develop a novel detection method. Using the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm, I find I can detect and connect staircase layers across datasets of hydrographic profiles from the Canada Basin in the Arctic Ocean. This offers an advantage over previous detection methods which treat each profile individually as, here, the sprawling horizontal nature of the layers can be analyzed. Using this clustering method, I identify layers in the Beaufort Gyre Region which span over 1000 km horizontally and persist for nearly two decades. In addition to reproducing many results from previous studies, I find the layers to be evolving in time. The layers are sinking at approximately the same rate as the overall downwelling in the region. I also find that layers in the upper staircase are warming while layers near the bottom are cooling. I develop a set of numerical experiments to examine the interactions between internal waves and idealized staircase stratification structures. For structures with one layer, I find the transmission of waves decreases monotonically as the layer thickness gets larger relative to the wavelength. With multiple layers present, I find peaks in transmission for particular ratios of thickness to wavelength, the patterns of which become more complex as more layers are added. I also reproduce the results of a laboratory experiment, finding the same pattern of reflection and transmission of waves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".