Spatial and Temporal Origins of the La Perouse Low Oxygen Pool: A Combined Lagrangian Statistical Approach
Bibliographic record
Abstract
Abstract A shortage of dissolved oxygen in seawater can adversely impact marine life and ecosystems. Low oxygen conditions at depth occur in many coastal regions, driven by both local productivity and remote changes in the source waters. A low‐oxygen dense pool of water is observed every summer over the mid‐shelf off southwest Vancouver Island in the Juan de Fuca Eddy region. We trace the dense pool waters back to their source using Lagrangian Particle tracking in an ocean model. The model accuracy is evaluated against a set of dense observations collected in August 2013. Only locations where the model represents water properties well are used as starting locations for the tracking. These locations are selected using a K‐Means clustering algorithm. Tracking particles backwards in time showed that the low oxygen dense pool is primarily composed of water from the California Undercurrent, shallower water from further offshore of the Washington shelf, and offshore water. Waters from the northern shelf were not a primary source even though summer currents are from the north. Correspondingly, the water primarily arrived in the south Vancouver Island region in spring, months before the cruise. A kernel density estimate shows the final water properties of the dense pool are well represented by this mixture. The dense water pathways up and onto the shelf are primarily through coastal submarine canyons in early summer. Combining high spatial resolution observations, a carefully evaluated numerical model, Lagrangian tracking, and statistical techniques reveal detailed answers not obtainable through a single method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".