Data acquisition, processing and management systems for a Canadian bridge monitoring project
Bibliographic record
Abstract
This paper will present a number of technical aspects for one of the most elaborate instrumentation and data acquisition projects ever undertaken in Canada. Confederation Bridge, the longest bridge built over ice covered seawater has been equipped with the state of the art data acquistition devices and systems as well as data transfer networks. The Bridge has been providing a fixed surface connection between Prince Edward Island and Province of New Brunswick in Canada since its opening in 1997. The Bridge has a rather long design service life of 100 years. Because of its large size and long span length, its design is not covered by any existing codes or standards worldwide. The focus of the paper is to introduce the data acquisition, transfer, processing and management systems. The instrumentation and communications infrastructure and devices will be presented in some details along with the data processing and management systems and techniques. Teams of engineers and researchers use the collected data to verify the analysis and design assumptions and parameters as well as investigate the short-term and long-term behaviour and health of the Bridge. The collected data are also used in furthering research activities in the field of bridge engineering and in elevating our knowledge about behaviour, reliability and durability of such complex structures, their components and materials.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".