Bibliographic record
Abstract
We present an initial draft of the “Luminous" interactive interpreter for a new dialect of the functional dataflow language Lucid. Luminous is not the first implementation of Lucid but it is novel in several ways. First, the dialect is two dimensional (has a space as well as a time dimension) with special space operators. And it performs dimensional analysis to determine, for each program variable, which dimensions may be required to evaluate a variable and which are irrelevant. This information is vital for producing output and for avoiding expensive redundancy in the cache. Also, Luminous is interactive: the user can demand the value of an arbitrary expression and immediately see the results in an appropriate form, depending on the dimensionality of the expression. The user can also alter existing definitions and re-demand the value of expressions, to see the effect of the change. Unlike previous Lucid implementation, Luminous operates directly on the source - it doesn’t build a parse tree. The source is ‘dismantled’ into elementary equations in a bottom-up procedure based on program transformation which skips building a parse tree. Dismantling is a non deterministic distributable dataflow procedure. This could become very important when (in the future) we deal with programs millions of lines long, when parsing is a major problem. The final result of dismantling is an (unordered) set of ‘atomic’ equations – so called because they cannot be simplified, each consisting of a simple variable equated to an expression consisting of a single operator applied to arguments, which are also simple variables (or literals). Converting a program to a set of atomic equations (another first for Luminous) vastly simplifies evaluation and static analysis. We discuss shortcomings of the current interpreter, the most glaring of which is the lack of user defined stream transformations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".