Expert system for fire and reactivity MSDS text
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Managing comprehensive and consistent material safety data sheet (MSDS) content for thousands of different chemical products is a significant challenge for any corporation. For a multinational company, the hazard information for each product should be consistent, no matter where it is sold. This paper will focus on one approach that has been successfully used to achieve this goal for the topics of fire and reactivity hazards. This paper discusses the development of an expert system to assist with the selection of fire and reactivity statements. The selection process is dependent upon the physical properties of the material as well as thermal stability and chemical reactivity information. In this paper, milestones in the development of this approach will be reviewed in addition to desirable features of expert system shells. The key properties used in the evaluations for each major topic are listed. Lessons learned from developing this approach are summarized and a few examples of topics triggered by selected properties are presented. © 2006 American Institute of Chemical Engineers Process Saf Prog, 2007
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it