Match-making in bartering scenarios
Bibliographic record
Abstract
This thesis extends tree similarity based match-making from the buyer/seller situa-tion to a scenario of bilateral bartering and multi-agent ring bartering. It is built on top of the AgentMatcher tree similarity algorithm for node-labelled, arc-labelled, arc-weighted trees. A representation of these trees in a multi-dimensional space is developed to allow efficient indexing and pruning in large tree databases. The con-cept of risk is introduced to control the process of bartering ring construction. We have tested our system on the Teclantic.ca portal, where it allows researchers and companies from Atlantic Canada to share technologies as well as to be contacted by investors. ii Acknowledgements First and foremost, I would like to express my appreciation to my supervisors, Dr. Virendra C. Bhavsar and Dr. Harold Boley, who gave me a great deal of support throughout my time at UNB. They continuously contributed their time, effort and thought in guiding and helping me during the research and writing of this thesis. Also, I am grateful to the Faculty of Computer Science for their support, and to my thesis committee members. Special thanks go to Ms. Linda Sales and all her administrative colleagues for their direction and help, and to all system support staff for their technical assistance. I also thank the AgentMatcher research group for their support and advice. In particular, I also thank Mr. Lu Yang and Mr. Marcel Ball whose work predates my own, and who helped me understanding their accomplishments. Finally, I am grateful to all other people in the Faculty of Computer Science who have assisted me in the course of this work. iii
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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) | 0.000 | 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".