Aspen dynamics after harvesting in boreal coniferous forest of Northwestern Quebec
Bibliographic record
Abstract
I would like to thank the people who supported and helped me immensely in this study; Prof. Annie DesRochers, my thesis supervisor, deepest gratitude for her invaluable assistance and support throughout my study. \nProf. Francine Tremblay, my co-supervisor, for her valuable insights, guidance and support throughout the genetic part of my thesis. \nI must also acknowledge the help of the lab and field technicians for their technical and assistance throughout my research; L. Blackburn, Y. Loranger, and A. Rouillard. I would also like to thank Marc Mazerolle for his help with statistical analyses. \nI would like to express my profound gratitude to Robert Simard for his help. Special thanks to my friends; Ingrid Cea Roa, Ines-Nelly Moussavou Boussougou, Erol Yilmaz, Jessica Marchall, Nicole Fenton and Francine Giagnard. \nFinally I wish to express my warm and deep appreciation to my parents and my sister for their understanding, unconditional love and faith in me. They have always supported and encouraged me to do my best in all aspects of life. \n
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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".