Description of the Determination Processes for the Typical Research and Development Intensity Normative Indicators
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
The article is devoted to description of the determination processes for the typical research and development (R&D) intensity normative indicators. In the theoretical part, the authors consider the standards system formation and labor costs norms for R&D. The main composit element (CE) hierarchy of the R&D technology is given. The scheme of the development algorithm for the R&D labor costs standards is drawn. The labor costs norming technique for research works is considered. The procedure for determining the labor costs normative volume for a standardized object is determined. In the research part, the article’s authors examined the automated system components used to determine labor intensity forecast indicators in the product life cycle information support. The process of determining the normative labor costs volume based on eight consecutive stages is presented. The database composition necessary for the product life cycle information support is described. Modules for projects’ planning and monitoring in the automated system framework are considered structurally. The modules’ composition used for the analysis of production systems and forecasting production economic indicators is determined. The regulatory requirements for the production’s modules for technological support and technical regulation are given as part of the automated system work for determining labor intensity forecast indicators in the product life cycle information support. The article concludes with an algorithm for estimating the R&D work clusters’ cost and the aircraft’s distributed systems creation and development.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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