Mercury storage and cycling in permafrost peatlands of the Hudson Bay Lowlands
Bibliographic record
Abstract
Pascale's lab members, including Tabatha Rahman, who I've gotten to share several amazing field seasons in Churchill with, to Rose-Marie Cardinal, for asking questions about Hg and forcing me to really think and understand the processes, to Dani Chiasson for her enthusiasm about peat, Churchill, and many things Arctic, and to more recent members of the lab Edith and Hemma, who I'm really happy to have spent time with at an excellent European Conference on Permafrost in Spain.Thanks Frederic Brieger for taking the time to come to Churchill and fly drone surveys, and the many pleasant conversations we've had when running into each other at the university.The research professionals in the CRYO-UL Lab, Sarah Gauthier, Arianne St-Amour, and Emmanuel (Manu) L'Hérault also provides so much help, good conversation, and good laughs that I will remember as a fond part of my PhD experience.Many thanks to Sam Hunter from Peawanuck
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".