2003 Rock Properties Database: Density, Magnetic Susceptibility, and Natural Remanent Magnetization of Rocks in Minnesota
Bibliographic record
Abstract
Geologic interpretation of gravity and magnetic anomaly data in a given area is greatly enhanced if density, magnetic susceptibility and natural remanent magnetization (NRM) data are available for representative rock-types. Along with outcrop and drill-hole information, rock property data help relate geophysical anomaly signatures to probable rock types, and provide constraints on the use of anomaly data as a tool for mapping and for modeling geology at depth. Most of the density and magnetization data contained in this database were acquired over the last two decades by the Minnesota Geological Survey (MGS) as part of an on-going program to collect rock properties. A group of Paleozoic samples were collected from Iowa and included in the database because they provide a representative suite of data for rocks present, but not widely exposed in Minnesota. Additional data were derived from studies by the U. S. Geological Survey (Bath, 1962; Beck, 1970; Beck and Lindsley, 1969; Books, 1972; Jahren, 1965), The University of Minnesota (Bleifuss, 1952, Mooney and Bleifuss, 1952), The University of Western Ontario (Palmer, 1970), and the Geological Survey of Canada (Dubois, 1962).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.008 |
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".