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
Multidatabase serializability is defined as an extension of the well-known serializability theory in order to provide a theoretical framework for research in concurrency control of transactions over multidatabase systems. Also introduced are multidatabase serializability graphs which capture the ordering characteristics of global as well as local transactions. Two schedulers that produce multidatabase serializable histories are described. The first scheduler is a conservative one which only permits one global subtransaction to proceed if all of the global subtransactions can proceed for any given global transaction. The 'all or nothing' approach of this algorithm is simple, elegant, and correct. The second scheduler is more aggressive in that it attempts to schedule as many global subtransactions as possible as soon as possible. A distinguishing feature of this work is the environment that it considers; the most pessimistic scenario is assumed, where individual database management systems are totally autonomous with no knowledge of each other. This restricts the communication between them to be via the multidatabase layer and requires that the global scheduler 'hand down' the order of execution of global transactions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it