The International Team in NanosafeTy (TITNT): A Multidisciplinary group for an improvement of Nanorisk Assessment and Management
Bibliographic record
Abstract
Nanotechnology allows the ability to design many new materials and devices with multiple applications, such as in medicine, electronics, and energy production. However, nanotechnology also raises several concerns about the toxicity and environmental impact of nanomaterials. A report published by the Council of Canadian Academies points out the necessity to respond about many uncertainties associated with risk assessment for ensuring the safety of health and environment. Nanotoxicology (or Nanosafety) is a part of the toxicology science that aims to study adverse effects of nanomaterials or nanoparticles on living organisms. This field includes different aspects from workers prevention to the environment protection. Group of researchers have initiated an international powerful interactive milieu for researchers to work in concert for a global and integrated study of many aspects of nanotoxicology. The International Team in NanosafeTy (TITNT) is composed of research scientists from 5 different countries (Canada, USA, Japan, France and Germany) working together on 6 different specific thematics, and organized as 9 different technology platforms (www.titnt.com). TITNT aims to study different features of nanomaterials related to nanosafety, such as in vivo and in vitro studies, life cycle, occupational protections and monitoring, early biomarkers detection, characterization and nanotoxicokinetic/dynamic assessment during and after nanoparticles synthesis and the societal, public policy and environmental aspects. While the rapid growth of nanotechnology is opening up a floodgate of opportunities, the legislation related is lagging behind mainly because of a lack of knowledge in the biosafety of most nanomaterials. The main goal of TITNT is to improve knowledge in nanosafety science for the benefit of the discipline, for better public policies and for the public itself.
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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 | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".