Bibliographic record
Abstract
This thesis outlines two advancements in the field of limited data absorption tomography. First, a novel reconstruction algorithm integrating the use of level set methods is presented that incorporates the additional a priori knowledge of a distinct interface between the species of interest and co-flow. The added a priori further reduces the ill-posedness of the system to produce a final concentration distribution that explains the laser absorption measurements and is qualitatively consistent with advection/diffusion transport. The algorithm is demonstrated by solving a simulated laser tomography experiment on a turbulent methane plume, and is compared with the current state-of-the-art reconstruction algorithm. \nGiven the limited number of attenuated measurements, accurate reconstructions are also highly dependent on the locations sampled by the measurement array. This thesis displays how the mathematical properties of the coefficient matrix, A, formed by the locations of the lasers, are related to the information content of the attenuation data using a Tikhonov reconstruction framework. This formulation, in turn, becomes a basis for a beam arrangement design algorithm that minimizes the reliance on additional assumed information about the concentration distribution. Using genetic algorithms, beam arrangements can be optimized for a given application by incorporating physical constraints of the beam locations. The algorithm is demonstrated by optimizing unconstrained and constrained arrangements of light sources and detectors. Simulated experiments are performed to validate the optimality of the arrangements.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".