Hot Rocks from Cold Places: A Field, Geochemical and Geochronological Study from the High Arctic Large Igneous Province (HALIP) at Axel Heiberg Island, Nunavut
Bibliographic record
Abstract
rock saw, "blender," and "Alvin" along with making my thin sections and the discussions about sample preparation process.Furthermore, I thank Etienne Menard and Matt Hanewitch for help in sample processing.Thanks to Dr. Shuangquan Zhang for help in guiding me through the process of turning rock powder into excellent Sm-Nd isotopic data including sample dissolution, ion exchange chromatography and mass-spectrometer loading and measurements.I also acknowledge Peter Jones in the microprobe lab here at Carleton University for helping me search for baddelyites in diabasic and gabbroic samples, Dr. Ulf, Soderlund at Lund University (Sweden) for separating these and Dr. Sandra Kamo (Jack Satterly geochronology lab,
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".