Bibliographic record
Abstract
Transport in the atmosphere and in the ocean is the result of the complex action of time-dependent and often highly turbulent flow.A useful diagnostic that summarizes the rate at which fluid elements are transported from some region to a point (or the reverse) via a multiplicity of pathways and mechanisms is the probability density function (pdf ) of transit times.The first moment of this pdf, often referred to as ''mean age,'' has become an important transport diagnostic commonly used by the observational community.This paper explores how to probe the flow with passive tracers to extract transit-time pdf's.As a foundation, the literal ''tracer age'' is defined as the elapsed time since tracer was injected into the flow, and the corresponding tracer-age distribution, Z, as the fractional tracer mass in a given interval of tracer age.The distribution, Z, has concrete physical interpretation for arbitrary sources, but is only equivalent to a tracer-independent transit-time pdf of the flow in special cases.The transit-time pdf is a propagator, GЈ, of boundary conditions (the ''age spectrum'' of T. M. Hall and R. A. Plumb) applied over a control surface, ⍀.The propagator GЈ is shown to be the flux into ⍀ resulting from a unit mass injected into the time-reversed flow.Through explicit construction of the transit-time pdf using the concept of tracer age, the special cases for which Z and GЈ coincide are established.This allows a direct physical demonstration of GЈ, and its adjoint GЈ † , as the pdf's of transit times since fluid at point r had last contact with ⍀, and until fluid at r will have first contact with ⍀, respectively.In the limit as ⍀ is shrunk to a point, point-to-point transit-time pdf's are well defined, but their mean transit time and higher-order moments become infinite.Several concrete geophysical examples are considered to illustrate under what conditions characteristics of tracer-age and transit-time pdf's can be inferred from observations in the atmosphere or the ocean.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".