Bragg grating laser sensing systems for smart structures
Bibliographic record
Abstract
A number of major challenges face the practical implementation of Smart Structure technology. One of the most important is concerned with the communication link between the structure's resident fiber optic sensing system and the support structure. In general this optical interface must be robust, have minimal structural perturbation, low cost, be easy to fabricate and integrate with the structure. If multiplexing is not undertaken within the structure, then each sensor would have its own input/output and would have to ingress/egress the structure. This represents a severe challenge, since the most suitable sensors are based on single mode optical fibers and this interconnect must be very user-friendly. We have embedded 20 Bragg grating sensors within 5 large concrete (65 ft long) girders that support the deck of a new two span road bridge in Calgary, and have developed a 4 - channel fiber laser Bragg grating sensor demodulation system that can interrogate any four of these Bragg grating sensors at one time. This Bragg grating demodulation system involves 4 independent erbium doped optical fiber lasers, each of which is tuned by a connectorized Bragg grating that is embedded within a concrete girder.>
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".