Supplemental material: Climatic forcing of erosion, landscape, and tectonics in the Bhutan Himalayas
Bibliographic record
Abstract
Appendix DR1 Materials and methods DatingSamples 360-5 to 360-19 were processed by Donelick Analytical and analyzed by A.Blythe (set 1), all other samples were processed by Alexander Grist at Dalhousie University and analyzed by I. Coutand (set 2): standard magnetic and heavy liquid mineral separation procedures were used.Apatites were mounted in araldite epoxy.Sample surfaces were ground and polished.Apatite mounts were etched in 7% HNO 3 at 18°C for 22s (set 1) and etched in 5M HNO 3 at 24°C for 20s (set 2).An "external detector" (Naeser, 1979), consisting of low-U (<5 ppb) muscovite, was used for each sample.Samples were irradiated in the Cornell University Triga nuclear reactor (set 1) and at the Dalhousie University Slowpoke reactor (set 2).Following irradiation, the muscovites were etched in 48% HF for 30 min at 18°C (set 1) or room temperature (set 2).Tracks were counted using a 100X dry lens and 1250X total magnification in crystals with well-etched, clearly visible tracks and sharp polishing scratches.A Kinetek stage and software (Dumitru, 1993) were used for analyses.Standard and induced track densities were determined on external detectors (geometry factor = 0.5), and spontaneous track densities were determined on internal mineral surfaces.Ages were calculated using ζ = 320 ± 9 for dosimeter glass SRM 962a for A. Blythe and ζ = 369.5 ± 5.1 for dosimeter glass CN5 for I. Coutand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.441 | 0.053 |
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".