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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".