Bibliographic record
Abstract
This time last year we announced that Macromolecular Bioscience (along with Macromolecular Theory & Simulations and Macromolecular Reaction Engineering) would be published online only starting with the first issue of 2013. Almost one year later, we can sum up that this change has been a positive experience through and through. From board members and authors we have received solely positive feedback. Our authors especially appreciate the free color figures in their published articles. In 2013, submissions to the journal increased by almost 20% compared to the previous year. The community was obviously ready for the change. But how about us editors? Certainly it was unfamiliar in the beginning to go to a conference without some printed copies of the journal in your hands. Luckily, our colleagues from the marketing department had some clever ideas on how to present our content in the digital world. QR codes are incorporated into promotional flyers to highlight outstanding publications. Times are changing: while I as an active researcher liked to read through journal copies at conferences a couple of years ago, nowadays people can be seen scanning the QR codes in those flyers and enjoying the free access to selected contributions. On top of that, we draw additional attention to carefully selected articles that we promote on MaterialsViews and MaterialsViewsChina (see Figure 1). There is no doubt that Macromolecular Bioscience is getting more attention than ever before. This is confirmed when looking at the number of full text downloads, which have gone up significantly. In the first 9 months of 2013, we observed a nearly 20% growth (compared to the same period in 2012). The papers with the highest numbers of downloads between November 2012 and October 2013 are given in Table 1. Many of these contributions deal with polymeric scaffolds and tissue engineering, but also topics such as biosensors, bioadhesion, drug delivery, and vaccines are covered. Regionally, a wide diversity can be seen. Many of these top papers come from the USA and Canada, but also contributions from China, Germany, and the Netherlands made it to the top list. I would like to thank the authors of these outstanding papers as well as our other authors from around the world for their trust, our critical reviewers and board members for their feedback, and you, our readers, for your ongoing interest in our journal. Together we are looking forward to an exciting 2014! Kirsten Severing Editor
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".