Unsupervised clustering of Lagrangian trajectories in the Labrador Current
Bibliographic record
Abstract
Lagrangian studies are a widely-used and powerful way to analyse and interpret phenomenons in oceanography and atmospheric sciences. Such studies can be based on dataset either consisting of real trajectories (e.g. oceanic drifters or floats) or of virtual trajectories computed from velocity outputs from model or observation-derived velocities. Such data can help investigate pathways of water masses, pollutants or storms, or identify important convection areas to name a few. As many of these analyses are based on large volumes of data that can be challenging to examine, machine learning can provide an efficient and automated way to classify information or detect patterns. Here, we present an application of unsupervised clustering to the identification of the main pathways of the shelf-break branch of the Labrador Current, a critical component of the North Atlantic circulation. The current flows southward along the Labrador Shelf and splits in the region of the Grand Banks, either retroflecting north-eastward and feeding the subpolar basin of the North Atlantic Ocean (SPNA) or continuing westward along the shelf-break, feeding the Slope Sea and the east coast of North America. The proportion feeding each area impacts their salinity and convection, as well as their biogeochemistry, with consequences on marine life. Our dataset is composed of millions of virtual particle trajectories computed from the water velocities of the GLORYS12 ocean reanalysis. We implement an unsupervised Machine Learning clustering algorithm on the shape of the trajectories. The algorithm is a kernalized k-means++ algorithm with a minimal number of hyperparameters, coupled to a kernalized Principal Component Analysis (PCA) features reduction. We will present the pre-processing of the data, as well as canonical and physics-based methods for choosing the hyperparameters. The algorithm identifies six main pathways of the Labrador Current. Applying the resulting classification method to 25 years of ocean reanalysis, we quantify the relative importance of the six pathways in time and construct a retroflection index that is used to study the drivers of the retroflection variability. This study highlights the potential of such a simple clustering method for Lagrangian trajectory analysis in oceanography or in other climate applications.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".