Bibliographic record
Abstract
In the Reordering Buffer Management (RBM) problem, we are given an input sequence of size N and a buffer B of size k (k < N ).Each item in the input sequence is characterized by a specific attribute referred to as its "colour".The defining characteristic of this problem is that a cost is incurred when we switch from one colour to another in the resulting output sequence.This cost can be uniform or non-uniform.Our goal is to permute the input sequence using the buffer B such that the cost of switching between colours is minimized.In the online version of the RBM problem, the input sequence is not known in advance.We propose a new algorithm in this thesis, Accelerated Threshold (Acc-T) for the online version of the RBM problem with non-uniform costs.This thesis also presents experimental results for several existing uniform and non-uniform cost algorithms, for the online version of the RBM problem.We compare these algorithms against different combinations of input sequence types, cost functions, buffer sizes and number of colours.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".