Bibliographic record
Abstract
The Kantorovich-Rubinstein-Wasserstein metric defines the distance between two probability measures μ \mu and ν \nu on R d + 1 {R^{d + 1}} by computing the cheapest way to transport the mass of μ \mu onto ν \nu , where the cost per unit mass transported is a given function c ( x , y ) c\left ( x, y \right ) on R 2 d + 2 {R^{2d + 2}} . Motivated by applications to shape recognition, we analyze this transportation problem with the cost c ( x , y ) = | x − y | 2 c\left ( x, y \right ) = {\left | {x - y} \right |^2} and measures supported on two curves in the plane, or more generally on the boundaries of two domains Ω , Λ ⊂ R d + 1 \Omega , \Lambda \subset {R^{d + 1}} . Unlike the theory for measures that are absolutely continuous with respect to Lebesgue, it turns out not to be the case that μ − a . e . x
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".