Bibliographic record
Abstract
Many web companies deal with enormous data sizes and request rates beyond the capabilities of traditional database systems. This has led to the de-velopment of modern Big Data Platforms (BDPs). BDPs handle large amounts of data and activity through massively distributed infrastructures. To achieve performance and availability at Internet scale, BDPs restrict querying capability, and pro-vide weaker consistency guarantees than traditional ACID transactions. The reduced functionality as found in key-value stores is sufficient for many web applications. An important requirement of many big data sys-tems is an online view of the current status of the data and activity. Typical big data systems such as key-value stores only allow a key-based access. In order to enable more complex querying mecha-nisms, while satisfying necessary latencies materi-alized views are employed. The efficiency of the maintenance of these views is a key factor of the usability of the system. Expensive operations such as full table scans are impractical for small, fre-quent modifications on Internet-scale data sets. In this paper, we present an efficient implementation of materialized views in key-value stores that en-ables complex query processing and is tailored for efficient maintenance. 1
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".