Although this program should run on Windows NT and 2000, these operating systems may have real time issues which have not been explored to this author's satisfaction.
Bibliographic record
Abstract
Presented in this paper is a real-time, object-oriented solution for programming experimental software on the Windows 9x 1 platform. Every attempt is taken to maximize the strengths (32 bit graphics, hardware acceleration) and minimize the weaknesses (OS and Input timing uncertainties) inherent with this operating system. Analysis is presented which clearly shows the capability of this system for real time studies, as well as the problems in assuming that Windows will do this for you. Sample code is presented on how to implement these ideas using C++ as the programming language and OpenGL for the graphics library. This research was partially funded by an NSERC post graduate scholarship with a Canadian Space Agency Supplement. Address correspondence to the author at: Department of Psychology, Dalhousie University, Halifax, N.S. Canada. B3H 4J1. Email macinnwj@cs.dal.ca Sample code can be downloaded at http://or.psychology.dal.ca/~joe/downloads/ My thanks to Patricia McMullen, Ray...
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.303 | 0.244 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".