Bibliographic record
Abstract
Most of current Mobile Agent systems are based on scripting or interpreted programming languages that offer portable virtual machines for executing agent code. Mobile agents are able to access many different resources on the Internet, and retrieve, analyze, manipulate, and integrate heterogeneous data and information on demand. These complex behaviors of agents need more expressive power and intelligence provided by their programming language. Naturally, Prolog is one of the best programming languages for the intelligent mobile agent paradigm. IMAGO Prolog [1] is a simplified Prolog with an extended Application Programming Interface (API) to support mobile agent applications. The implementation of IMAGO Prolog is based on a Multi-threading Logic Virtual Machine (MLVM) [2]. The MLVM adopts a novel memory management approach, such as the single stack scheme and Chronological Garbage Collection (CGC) [3]. I designed and implemented the IMAGO Prolog compiler, which translates IMAGO Prolog programs into MLVM bytecodes. The IMAGO Prolog compiler consists of a preprocessor, lexical analyzer, syntax analyzer, code optimizer, code generator and assembler.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".