WCTE 2016 e-book : containing all full papers submitted to the World Conference on Timber Engineering (WCTE 2016), August 22-25, 2016, Vienna, Austria
Bibliographic record
Abstract
The World Conference on Timber Engineering (WCTE) is the world´s premier forum for presenting and discussing the latest technical and architectural developments and innovations in wood or timber construction. Since 1996, the WCTE has been held biennially in different parts of the world. The scientific program of WCTE 2016 included 800 presentations – 350 out of them in the on specific topics organized mini-symposia, more than 200 in general sessions related to conference topics and 190 additional poster presentations. The presentations showing latest research results were distributed along the five conference tracks as follows: one third each are related to “Track 1: wood products & components” and “Track 4: timber engineering”. The remaining third is evenly spread among “Track 2: computer-based methods”, “Track 3: timber architecture”, and “Track 5: implementation”. The scientific program was complemented by Plenary and Semi-Plenary Lectures of distinguished scientists in the field: PLENARY LECTURES by Ingo Burgert (Switzerland), Gerald Epp (Canada), Hermann Kaufmann (Austria), and Erik Serrano (Sweden), as well as SEMI-PLENARY LECTURES by Ki-Cheol Bae (Korea), Andy Buchanan (New Zealand), Kaori Fujita (Japan), Esa Kaikkonen (Finland), Alfred Kammerhofer (Switzerland), Eric N. Landis (USA), Anders Olsson (Sweden,) Maurizio Piazza (Italy), Alfred Teischinger (Austria), Jan-Willem Van de Kuilen (Germany/Netherlands), Stefan Winter (Germany) und Gary C. Williams (Canada) More information on WCTE 2016 is available at: http://wcte2016.conf.tuwien.ac.at/ The e-book contains 6,301 pages and has a size of 1.3 GB. Hence, please be patient when downloading it.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.711 | 0.696 |
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".