Governor Frank Keating’s TAR CREEK SUPERFUND TASK FORCE Drainage and Flooding SubcommitteeEXECUTIVE SUMMARY
Bibliographic record
Abstract
Prior to mining activities in Ottawa County, water was considered to be one of the area's greatest resources. In 1902, the water was described to be "clear and sparkling. " One author predicted that "The Neosho River and Tar Creek would furnish a never failing supply of the purest and cleanest (water). " Nearly 100 years later, these same streams are contributors to the environmental problems in the area due to poor drainage and flooding as a result of mining activities. The Tar Creek drainage area north of Miami, Oklahoma, has been greatly disturbed by more than 80 years of mining activity that has resulted in a system of poorly draining streams that are commonly bankfull of water during non-flood periods. Without modification, the Tar Creek drainage system will continue to function as it presently does with frequent flooding being experienced in the area due to the hydraulic inefficiency of the streams. This report was prepared at the request of the Tar Creek Superfund Task Force which was established by Executive Order 2000-02 dated January 20, 2000 and signed by Governor Frank Keating. The Drainage and Flooding Subcommittee consisted of volunteers from
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.047 |
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".