Bibliographic record
Abstract
The Warren Abstract Machine has been accepted as the de facto standard for implementing Prolog for more than ten years. The WAM adopts structure copying to represent Prolog terms. It flattens nested structures and expands them into efficient WAM instructions which either copy terms into the heap or unify terms along the heap pointer. When two terms to be unified are structure instances in the heap, the WAM must invoke a built-in procedure to carry out the stack based full unification. We propose a new Prolog execution model-the Lakehead Abstract Machine. The LAM is designed to retain the control features of the WAM, but the structure copying is replaced by program sharing for implementing unification. The idea of program sharing is originated from the structure sharing used in the DEC-10 Prolog. The significant difference, however, is that the shared resources are no longer structure prototypes, but executable LAM code. With the LAM, nested structures are flattened and then translated into a set of subprograms. In constructing a structure, the LAM only puts its corresponding subprogram pointer into the heap. When two terms to be unified are structures, their bound subprograms will be invoked. We have implemented an experimental LAM emulator in C. Benchmarks show that the LAM based Prolog implementation is quite competitive with the WAM based systems, such as SICStus and BIN-Prolog.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".