Comparative testing of high performance fabrics in the wet state
Bibliographic record
Abstract
Razvojem visokoucinkovitih tkanina i ispitivanjem njihove otpornosti na habanje normiranom metodom po Martindaleu doslo se do spoznaja da su one izuzetno otporne te da bi se postojeca metoda možda trebala modificirati. Glede uvjeta, uobicajeno se otpornost na habanje ispituje u suhom stanju, ali otpornost u mokrom ne, a niti se tome posvecuje dovoljna pažnja. Naime, visokoucinkovite tkanine se najcesce koriste za vojnu, policijsku, sportsku i sl. namjenu u vrlo zahtjevnim uvjetima koji su daleko od idealnih. Koliko puta vojnik ili alpinista pokisne, tada se njegov opasac s oružjem ili razlicitom opremom haba o odjecu i sl., cime ljudski život može biti ozbiljno ugrožen. U skladu s navedenim, podatak o otpornosti na habanje u mokrom stanju bio bi dobra smjernica o ponasanju i trajnosti koristenih visokoucinkovitih tekstilnih materijala u zahtjevnim uvjetima uporabe. U ovom radu provedeno je komparativno ispitivanje habanja visokoucinkovitih tkanina u suhom i mokrom stanju. Dobivena saznanja i rezultati uspoređivani su s rezultatima dobivenim normiranim postupkom, tj. postupkom ispitivanja otpornosti na habanje u suhom stanju (HRN EN ISO 12947-3:2008). Ispitivanje otpornosti na habanje u suhom i mokrom stanju provedeno je pri 1000, 5000, 25000, 50000 i 100000 ciklusa s ciljem dobivanja signifikantnih rezultata. Kao dodatan pokazatelj izracunat je relativni gubitak mase u mokrom stanju (fw). Habanje u suhom i mokrom stanju provedeno je na uzorcima visokoucinkovitih tkanina, tj. na uzorcima deklariranim kao Yukon, Cordura®, Puma, Themsa i Windmaster®. Navedenim uzorcima određene su osnovne konstrukcijske karakteristike.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".