Structural Health Monitoring of Intelligent Infrastructure Conference 2017
Bibliographic record
Abstract
The Structural Health Monitoring (SHM) of road traffic bridges and heritage buildings and the reliability assessment of the design of structural members and systems have both received a great deal of attention by researchers in Australia and worldwide, with many methods of monitoring and analysis now being developed. The objectives of the SHMII-8 Conference are to promote and advance the Field of Structural Health Monitoring in Australia and the southern hemisphere, specifically to showcase achievements, exchange ideas and disseminate knowledge nationally and internationally, and to raise general community awareness on the need for, and value of, SHM research and application. With a theme of Structural Health Monitoring in Real-world Application, the SHMII-8 Conference will feature a strong program of keynote lectures, invited lectures, technical visit and touring activities. The three-day Conference program will include keynote lectures, invited lectures, technical visit and touring activities. English will be the official language of the Conference for both oral and written presentation. ISHMII holds the SHMII official conference every two years and once or more official workshops (such as CSHM), usually in alternating years. SHMII-8 will build on the success of SHMII-1, held in Tokyo 2003, SHMII-2 in Shenzhen 2005, SHMII-3 in Vancouver 2007, SHMII-4 in Zurich 2009, SHMII-5 in Cancun 2011, SHMII-6 in Hong Kong 2013 and SHMII-7 in Turin 2015. Chair Professor Tommy Chan Co-chair Dr Saeed Mahini
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".