Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many modern key-value stores rely on an in-memory index to map the location of each data entry in storage. The size of this index often becomes a memory bottleneck that makes it difficult to scale the system to large data sizes. To address this problem, the state-of-the-art approach is to structure this index as a succinct perfect hash table using only ≈ 4 bits per key. The downside is that the hash table encoding is computationally expensive to parse and may harm overall system performance. We introduce Sphinx, a succinct perfect hash table reengineered for high performance on commodity CPUs. Sphinx is encoded in a manner that lends itself to efficient access using rank and select primitives, and it uses auxiliary metadata to decode common hash table slots instantaneously. Sphinx is also expandable and parallelizable. We compare Sphinx to the best alternatives and show that it leads to a 2x reduction in query latency, update latency, and memory footprint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 it