Bibliographic record
Abstract
Unwanted exposures to high-energy or ionizing radiation can be hazardous to health. Prolonged or accumulated radiation dosage from either particle-emissions such as alpha/beta, proton, electron, neutron emissions, or high-energy electromagnetic waves such as X-rays/γ rays, may result in carcinogenesis, cell mutations, organ failure, etc. To avoid occupational hazards from these kinds of exposures, researchers have traditionally used heavy metals or their composites to attenuate the radiation. However, protective gear made of heavy metals are not only cumbersome but also are capable of producing more penetrative secondary radiations which requires additional shielding, increasing the cost and the weight factor. Consequently, significant research efforts have been focused toward designing efficient, lightweight, cost-effective, and flexible shielding materials for protection against radiation encountered in various industries (aerospace, hospitals, and nuclear reactors). In this regard, polymer composites have become attractive candidates for developing materials that can be designed to effectively attenuate photon or particle radiation. In this paper, we review the state-of-the-art of polymer composites reinforced with micro/nanomaterials, for their use as radiation shields.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".