Bibliographic record
Abstract
In this paper, an open problem posed by the second author [On $q$-series and split lattice paths, Graphs and Combinatorics, 2020] is addressed. Here, we provide combinatorial interpretations of four generalized basic series in terms of split lattice paths. Out of these series, two series have been studied by Adiga et. al. [On Generalization of Some Combinatorial Identities, J. Ramanujan Soc. of Math. and Math. Sc., 2016] using split $(n + t)$-color partitions and $R$-weighted lattice paths but a direct one-to-one correspondence between these two classes was missing. We are successful in the quest of establishing bijections between the combinatorial graphical interpretations in terms of split lattice paths and combinatorial interpretations in terms of split $(n + t)$-color partitions using a purely algebraic approach. In this process, we encounter Rogers--Ramanujan type identities and we are able to provide their graphical interpretations using a constructive approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".