Bibliographic record
Abstract
An overview of the compact yet powerful Lua programming language. Lua is a free and open-source multi-paradigm programming language released under the MIT license. Created in 1993 by Roberto Lerusalimschy, Luiz Henrique de Figueiredo and Waldemar Celes, Lua is a dynamically typed language. Extremely compact (only 150KB compiled), it is primarily used as a scripting language or an extension to another language (mainly C/C++). What Is Lua and How Is It Used? Lua is implemented as a library and has no “main” program. It works only when embedded in a host client. The interpreter is a C program that uses the Lua library to offer a standalone Lua interpreter. Rather than provide a complex and rigid specification for a single paradigm, Lua is intended to be extended to fit different problem types. Being small, Lua fits on many host platforms and has been ported and used in video games for both the PlayStation Portable and the Nintendo DS, and it is used in larger games, such as FarCry and World of Warcraft. The Adobe Photoshop Lightroom photography program and the lighthttpd Web server have incorporated Lua as well. Lua has a few advanced features, primarily coercion, coroutines, garbage collection, first-class functions and dynamic module loading. Because Lua is small, it includes only a few data types. It attempts to maintain a balance between power and small size. What's Different about Lua? Lua is comparably as easy as Python in terms of learning how to write code. Of the two, Lua is usually the better choice for embedded systems, simply because it's smaller. Lua's strength is in processing strings and tables. It handles logical equations more adeptly than Python. For a quick hack, a Lua programmer can process complicated data more quickly and easily than a Python programmer can (although a Ruby programmer can do so almost as quickly). But, for a large application that handles many chunks of complex data, a heavier language such as Ruby or Python may be a better choice. There is no need to worry about different types of integers. You may have found that the different types of integers and numbers (such as floats, longs or doubles) can screw up the output of your program or even make it crash if you are absent-minded. Lua uses coercion for every integer and number type to convert it into a single type. You can add a float, long integer or a double to any other type of integer or number without a hitch in Lua. In contrast, doing this can cause programs written in Python 2.4 or older versions to crash. Lua is extremely forgiving syntactically. What if, for some reason, you are A Look at Lua http://0-delivery.acm.org.innopac.lib.ryerson.ca/10.1145/1280000/12... 2 of 8 8/27/2007 8:24 PM programming on an embedded device with a four-inch wide screen? You can reduce the amount of lines and other characters, which in turn enables easy reading of the code to make up for the small screen. Small is beautiful. A programmer can embed Lua into several other languages, such as C/C++ and Java, without bloating the host language, because Lua has a tiny API. Similar to Lisp's single data structure, tables are the only data structuring mechanism that Lua has. This makes tables very powerful, because with a little work, they can emulate data structures in larger languages. Object-oriented programming implementation is minimalistic. Lua uses tables and functions rather than classes. In contrast to Python, Lua does not focus on 100% backward compatibility. Many newer releases of Lua break programs written in previous versions. Fortunately, the Lua developers always announce what the new versions of Lua will break. Lua supports threading. Multiple Lua interpreters can coexist in the same process, and each one can run independently in its own thread. This often makes Lua desirable for multithreaded programs in embedded systems.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 0.070 |
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".