Comparison of physical connectivity particle tracking models in the Flemish Cap region
Bibliographic record
Abstract
<strong>ABSTRACT</strong> Lagrangian particle tracking models are considered an important tool for assessing connectivity in the deep sea. A number of user interfaces are available to assess oceanic structural connectivity. These use currents produced by state-of-the-art ocean models, and can be used to run forward/hindcast simulations, habitat connectivity calculations, comparison of physical circulation models, etc. We compared simulation outputs from two particle tracking packages, WebDrogue v.0.7 and the Parcels framework version 2.1, the former having been previously published in a study investigating connectivity patterns among closed areas in the NAFO Regulatory Area. We further tested a combination of parameters used by Parcels (number of particles, particle spacing, time step, random walk) to determine optimal values for future applications. Parcels identified more connectivity than WebDrogue with differences attributed to higher current velocities in the underlying ocean model, although drift pathways were generally similar in both. <strong>RÉSUMÉ </strong> On considère que les modèles Lagrangien de suivi de particules sont un outil important pour évaluer la connectivité en haute mer. Il existe un certain nombre d’interfaces utilisateur permettant d’évaluer la connectivité structurelle des océans. Ces interfaces, qui utilisent les courants issus de modèles océaniques de pointe, peuvent entre autres servir à effectuer des simulations prospectives ou rétrospectives, à faire des calculs liés à la connectivité de l’habitat ainsi qu’à comparer des modèles de circulation physique. Dans le cadre de la présente étude, nous avons comparé les résultats de simulation issus de deux interfaces de suivi de particules, soit la version 0.7 de WebDrogue et la version 2.1 de Parcels. Les résultats de simulation issus de WebDrogue ont déjà été publiés dans le cadre d’une étude sur les tendances de connectivité au sein de zones fermées situées au sein de la zone réglementée par l’OPANO. Nous avons donc testé une combinaison de paramètres utilisés par Parcels (nombre de particules, espace entre les particules, intervalle de temps, marche aléatoire) afin de déterminer les valeurs optimales pour des applications futures. Les résultats montrent que la connectivité calculée par Parcels est plus élevée que celle calculée par WebDrogue; les différences sont attribuables à des vitesses de courants plus élevées dans le modèle océanique sous-jacent, même si les trajectoires de dérive sont généralement similaires pour les deux interfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".