Development of a collaborative tool for data valorisation in SMEs
Bibliographic record
Abstract
In recent years, solutions based on Industry 4.0. technologies have become more and more accessible for SMEs. As a result, better data collection strategies are being developed and an increase in available data has been observed. Furthermore, SMEs contribute largely to the GDP in certain industrial sectors such as the food industry, food services and the health sector. These SMEs are therefore naturally oriented towards Industry 4.0. solutions such as decentralized data collection and decision-making techniques. To an identical industrial sector, these SMEs can be considered a decentralized network of companies. There is interest in improving the overall performance of this type of network, while optimizing the individual performance of each company. In this paper, we propose a collaborative tool and a methodology to visualize and improve the performance of SME networks. The methodology is implemented through a case study in the agri-food sector in the Canadian forage industry. The collaborative platform that is developed enables a visualization of production performance, management techniques and the specific aspects of each individual company. Then, it proposes a methodology for the improvement of each company through best practices that are identified in similar contexts within the network.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".