Bibliographic record
Abstract
Nanotechnology can increase the functionality and durability of building materials like cement, insulation, windows, and weatherproofing. The introduction of nanomaterials to the construction industry can give buildings and infrastructure some degree of: self-repairing, self-cleaning, bacterial- and weather-resistant qualities, increased electrical conductivity, and the ability to break down pollution. Though nanotechnology is still in the early stages of development and can be expensive, increased research may find even more uses for nanotech and lower the cost of its implementation. La nanotechnologie peut augmenter la fonctionnalité et la durabilité des matériaux de construction comme le ciment, l'isolation, les fenêtres et les intempéries. L'introduction des nanomatériaux à l'industrie de la construction peut donner bâtiments et des infrastructures un certain degré d’auto-réparation, auto-nettoyage, résistance aux bactéries et intempéries, conductivité électrique augmenté, et la capacité de décomposer la pollution. Bien que la nanotechnologie est encore dans les premiers stades de développement et peut être cher, nouvelles recherches peuvent trouver encore plus d'utilisations pour la nanotechnologie et comment réduire sa coût.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".