Investigating the impact of direct effects of radiative forcing on ocean heat uptake
Bibliographic record
Abstract
I'd like to acknowledge Dr. Timothy Merlis for his exceptional supervision of the research presented.The developments could not have happened without his advice and input as well as his unrelenting encouragement throughout the process.He is approachable and optimistic, qualities that are exemplary for a great supervisor, and he demonstrates that great science begins with a curious mind.Furthermore, I thank the Faculty and Staff of the Department of Atmospheric and Oceanic Sciences for their support in the pursuit of my degree, in particular I recognize Carolina Dufour for her review of this thesis.I acknowledge the Earth System Grid Federation (ESGF) for providing the CMIP5 data sets, the Geophysical Fluid Dynamics Laboratory (GFDL) for making available the ocean-only model MOM5 to run simulations on, Compute Canada for providing computation allocations, and the Natural Sciences and Engineering Research Council (NSERC) of Canada for financial support of this project.In addition, I thank Frédéric Laliberté for the development of CDB Query, used to download CMIP5 data.Lastly, my deepest gratitude goes to my friends and family, both near and far, whose encouragement enabled me to succeed to the end.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".