Update of the Mineral Resources Data System for California including Mineral Deposit Types
Why this work is in the frame
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Bibliographic record
Abstract
There are more than 42,000 entries in the USGS Mineral Resources Data System (MRDS) for the State of California. Previously, the field for deposit type was sparsely populated, which made it difficult to evaluate mine sites needing environmental remediation. This has been rectified by populating the deposit type field using 100 deposit types cited in previous USGS publications, and 137 provisional deposit types described here. Other categories listed in the deposit type field include 29 types of mineral processing facilities, 6 miscellaneous categories used previously in MRDS, and 56 unclassified categories tied to primary commodity. References are provided for each deposit type, as available, including descriptive models, grade-tonnage models, and geo-environmental models. The new information on deposit type will be useful to federal, state, and local agencies concerned with prioritizing abandoned mine sites for environmental assessment. A version of the MRDS database for California with deposit types is provided in the files MRDS_CA_Enhanced_with_Deposit_Moel_Data.xlsx. and .csv. Other updates from the version of MRDS available at https://mrdata.usgs.gov/mrds/ include a column identifying duplicate entries (column C, dup_id) and improved consistency in columns representing commodity type (column K, com_type) and primary commodity (column L, commod1). No effort was made to remove or merge duplicate records. Deposit model number (column X, model_no) and model name (column Y, model_name) are explained in the metadata, and in tables 2 through 7. A data dictionary for tables 2 through 7 is provided in table 1. Table 2 T2_Depsosit-type-comparison.xlsx and .csv includes a comparison of USGS model types and provisional model types described here with mineral deposit classification systems used in Brazil and Canada. Table 3 T3_California-deposit-types.xlsx and .csv includes reference citations for the USGS deposit types identified in California, information on commodities and tectonic classification, and an example of each deposit type in California and outside California. Table 4 T4_Provisional-deposit-types.xlsx and .csv lists descriptions of provisional deposit types and an example within California. Table 5 T5_Miscellaneous-categories.xlsx and .csv lists several miscellaneous categories included in MRDS. Table 6 T6_Deposit-types-by-county.xlsx and .csv lists the number of occurrences of each deposit type in each of the 58 counties of California. Table 7 T7_Deposit-types-by-county-sorted-by-abundance.xlsx and .csv lists the same information as in Table 6 sorted by the total number of occurences of each deposit type. The file References_Tables2-4.pdf includes references cited in Tables 2, 3, and 4.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it