Bibliographic record
Abstract
This thesis presents a unified investigation into data-driven intelligent systems through three major contributions: (i) a research study introducing a hierarchical two-level genetic algorithm for automated feature engineering, (ii) Query Weave, a conversational structured-data analysis system, and (iii) ImmiAI, a retrieval-augmented immigration assistance chatbot grounded in authoritative Canadian sources. The first contribution develops a novel genetic algorithm that balances predictive performance and interpretability through multi-objective optimization, ensemble evaluation, and non-linear feature transformations. The second contribution addresses the limitations of large language models in tabular reasoning by proposing a layered architecture for schema profiling, statistical discovery, and service routing. The third contribution integrates web scraping, data-lake pipelines, vector retrieval, and grounded LLM reasoning to support accurate and auditable immigration guidance. Together, these components demonstrate how automation, metadata-driven analytics, and grounded natural-language interfaces can reduce technical barriers and expand access to AI systems. The thesis highlights the importance of verifiable computation, modular reasoning pipelines, and tool-augmented conversational design in the development of trustworthy intelligent systems.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".