Airborne electromagnetic and magnetic survey, Goldstream Creek watershed, interior Alaska, March 6th to March 17th, 2016
Bibliographic record
Abstract
The Goldstream geophysical survey dataset provides airborne electromagnetic and magnetic data and resistivity models from a 258-square-km portion of the Goldstream Creek watershed, interior Alaska. The survey was flown from March 6th to March 17th, 2016, by CGG Canada Ltd. using their RESOLVE airborne geophysical system. Survey line spacing was either 100 meters (m) or 200 m; additional custom lines were collected over areas of scientific interest. The data were measured from 30 m above the ground surface using a helicopter-towed sensor platform ('bird') on a 30-m long line. The geophysical survey provides a snapshot of subsurface electromagnetic and magnetic variability throughout the study area. The data and resistivity modeling facilitates interpretation of surface and subsurface features and processes within Goldstream Valley and surrounding areas of interior Alaska. The Goldstream Valley Watershed project is funded by the National Science Foundation, Office of Polar Programs, Arctic System Science Program, award #1500931. Users can access the originating data and additional project metadata from the Alaska Division of Geological andamp; Geophysical Surveys (DGGS) website: http://doi.org/10.14509/29681.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".