The path‐density distribution of oceanic surface‐to‐surface transport
Bibliographic record
Abstract
A novel diagnostic for advective‐diffusive surface‐to‐surface paths is developed and applied to a global ocean model. The diagnostic provides, for the first time, a rigorous quantitative assessment of the great ocean conveyor's deep branch. A new picture emerges of a diffusive conveyor in which the deep North Pacific is a holding pen of long‐residence‐time water. Our diagnostic is the joint density, η, per unit volume and interior residence time, τ, of paths connecting two specified surface patches. The spatially integrated η determines the residence‐time partitioned flux and volume of water in transit from entry to exit patch. We focus on interbasin paths from high‐latitude water mass formation regions to key regions of re‐exposure to the atmosphere. For non‐overlapping patches, a characteristic timescale is provided by the residence time, τϕ, for which the associated flux distribution, ϕ, has its maximum. Paths that are fast compared to τϕ are organized by the major current systems, while paths that are slow compared to τϕ are dominated by eddy diffusion. Because ϕ has substantial weight in its tail for τ > τϕ, the fast paths account for only a minority of the formation‐to‐re‐exposure flux. This conclusion is expected to apply to the real ocean based on recent tracer data analyses, which point to long eddy‐diffusive tails in the ocean's transit‐time distributions. The long‐τ asymptotic path density is governed by two time‐invariant patterns. One pattern, which we call the Deep North Pacific pattern, ultimately dominates a secondary redistribution pattern.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".