Erratum: The critical role of water in spider silk and its consequence for protein mechanics (Nanoscale (2011) 3 (DOI: 10.1039/c1nr10502g))
Bibliographic record
Abstract
The critical role of water in spider silk and its consequence for protein mechanics Cameron P. Brown, Jennifer MacLeod, Heinz Amenitsch, Fernando Cacho-Nerin, Harinderjit S. Gill, Andrew J. Price, Enrico Traversa, Silvia Licoccia and Federico Rosei Nanoscale , 2011, 3 , (DOI: 10.1039/c1nr10502g). Amendment published 18 th August 2011. The list of authors and affiliations should read as follows: Cameron P. Brown,* abcd Jennifer MacLeod, c Heinz Amenitsch, e Fernando Cacho-Nerin , e Harinderjit S. Gill, a Andrew J. Price, a Enrico Traversa, df Silvia Licoccia, bd and Federico Rosei, cd a Botnar Research Centre, University of Oxford, Oxford, UK. E-mail: cameron.brown@ndorms.ox.ac.uk b Centro NAST, Dipartimento di Scienze e Tecnologie Chimiche, Universita ` di Roma Tor Vergata, via Della Ricerca Scientifica, 000133, Roma, Italy c Institut National de la Recherche Scientifique, Universite du Que bec, 1650 boulevard Lionel-Boulet, Varennes, QC, Canada. E-mail: rosei@emt.inrs.ca d Italy-Quebec Joint Laboratory in Nanostructured Materials for Energy, Catalysis and Biomedical Applications e Institute for Biophysics and Nanosystems Research, Schmiedlstr. 6, 8042, Graz, Austria f International Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.042 |
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".