The BIOCEM Project: The Industrial Valorization of a Patent Through the “Proof of Concept” Program
Bibliographic record
Abstract
The aim of BIO-CEM project is focused to test an innovative patented concrete aeration system in scale-up pre-industial production of aerated autoclaved concrete blocks. The aeration system refers to the Bio-Aerated Autoclavated Concrete (BAAC) international patent n°102017000101039 - WO/2019/049005, developed by the ENEA researchers. This activity research is included in the “Proof of Concept Program” granted by ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) in 2018, aiming to reduce the gap between research results and industrial application. The targets of BAAC innovation are addressed to energy saving, CO2 reduction and environmental sustainability, obtained also by complete recycling of waste materials. In this regard, preliminary experiments were conducted in laboratory to test different types of mix design for the optimization of quality parameters such as: mechanical strength, thermal insulation and environmental impact. These tests are the first step of the industrial scale up, made in partnership with EKORU srl that has contributed with its own expertise and industrial plant facility.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".