A theory of fine-grained lineage for functions on structured objects
Bibliographic record
Abstract
Lineage is the process of keeping track of the relationship between the inputs of a data processing task and the parts of the output they contribute to produce. Depending on its precise definition, lineage can be seen as a form of database provenance, a means of tracking information flow in computer programs, or be used to express causality and provide counter-examples for the falsity of a logical statement. In this paper, we establish the formal foundations of a notion of lineage for arbitrary abstract functions manipulating objects that are “composite” –that is, can be made of multiple other objects. Three definitions of lineage over functions are formally defined, respectively called explanation, participation and extraction; we then establish explanation relationships for a set of elementary functions , and for compositions thereof. A fully functional implementation of these concepts is finally presented and experimentally evaluated.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".