Bibliographic record
Abstract
In non-classical mereologies, not every pair of underlapping elements has a sum. Hence, it begs the question as to what types of elements have sums, as well as its relationship to mereologies. Beyond intrinsic academic value, these questions are also of pragmatic interest - previous works have shown that non-classical mereologies are essential and not just optimal for the representation of gene sequences, time intervals, and machine scheduling plans. The goal of this thesis is hence to provide a new representation theorem for extensional and classical extensional mereology, for the purposes of clarifying the relationship between classical and non-classical mereologies. We provide a total of 5 representation theorems, utilizing algebra, set theory and graph theory for finite models of extensional mereology and classical extensional mereology, as well as a conjecture to guide future work in infinite models of classical extensional mereology.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".