COAST: Heat flow derived from Bottom Simulating Reflectors (BSR) at the Cascadia Subduction Zone (investigators H. Paul Johnson, Evan Solomon, Robert Harris, Marie Salmi)
Bibliographic record
Abstract
This heat flow data set was derived from Bottom Simulating Reflectors (BSR) imaged using active-source multi-channel seismic data acquired during the Cascadia Open Access Seismic Transects (COAST) survey conducted in 2012 along the Southern Cascadia Margin during R/V Langseth expedition MGL1212. The heat flow data and processing steps are described in Salmi et al., 2017. From each multi-channel seismic profile, the seafloor and BSR travel times were picked, and assuming 50% lithostatic/hydrostatic pressure and normal saline water, the temperatures were estimated. Thermal conductivity was assumed to follow the relationship provided by Davis et al., 1990 which correlates well with borehole data off Vancouver Island. The data file is in ASCII format and contains individual profile line number, common depth point (CDP), latitude, longitude, heat flow and its uncertainty, and seafloor depth derived from multibeam bathymetry also collected during cruise MGL1212. Funding was provided by NSF grants OCE11-44164 and OCE12-49552. Raw geophysical and seismic data can be downloaded from the Lamont-Doherty Earth Observatory Web site (http://www.marine-geo.org/tools/search/entry.php?id=MGL1212). Seismic profiles processed shipboard through post-stack time migration can be downloaded from the University of Texas Institute for Geophysics seismic database (http://www-udc.ig.utexas.edu/sdc/cruise.php?cruiseIn=mgl1212).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".