An Efficient Type-Agnostic Approach for Finding Sub-sequences in Data
Bibliographic record
Abstract
In this paper, we present an efficient type-agnostic approach for finding sub-sequences in data, such as text documents or GPS trajectories. Our approach relies on data deduplication for creating an inverted index. In contrast with existing data deduplication techniques that split raw sequences of characters arbitrarily, our approach preserves the semantics of the original sequence via the notion of token and can be used to index normalized data. When compared to indexing methods that preserve the semantics and operate on normalized data, our method increases the relevance of the inverted index, reduces its size and improves its performances. As data normalization is generally not used beyond the scope of textual data, we introduce a framework that helps identify the extent to which data should be normalized regardless of its type. On this basis, we demonstrate with a dataset made of GPS trajectories that our method can be used agnostically: it can be used to index and query data of a completely different type. Finally, we show that the resulting spatial-index is characterized by a better discrimination than classic spatial-indexing approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".