A Holistic Multi-Domain Association Model for Industrial Data
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
Data is collected from different industrial domains. Organizing that data makes change anticipation more planned and streamlined. This paper introduces a novel holistic model of associating different domains of industrial data. The model establishes a tree graph called cladogram to create a unified classification of data from market segments, product design and manufacturing capabilities and it is expandable beyond these domains. The cladogram is produced by the widely used biological Cladistics analysis, without modification. This approach has a great degree of simplicity without introducing an extra layer of mathematical modelling, while resulting in a data-inclusive graphical representation. A case study of automated and flexible assembly is presented to demonstrate the effectiveness of the model and its simplicity. Model results are significant, since they could reveal associations of the definitions of the objects from different data domains, which were used later in response to future changes in those domains.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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