ii Characterization of Coal Combustion Residues III ACKNOWLEDGMENTS
Bibliographic record
Abstract
Authors are grateful to the input provided by G. Helms, U.S. EPA, Office of Solid Waste and Emergency Response (Washington, D.C.) in helping with the research design and application of improved leaching test methods to provide better characterization data for fly ash and other coal combustion residues. Overall project planning and integration was carried out jointly by D.S. Kosson and F. Sanchez (Vanderbilt University), and P. Kariher (ARCADIS). R. Delapp and D. McGill of Vanderbilt University were responsible for the chemical analyses of the leachate samples except for mercury analysis. All other laboratory testing including physical and chemical analysis, sample digestion, and leaching tests of fly ash and other coal combustion residues was conducted by ARCADIS. Technical assistance was provided by A. Garrabrants of Vanderbilt University. Solid phase chromium analysis by X-ray Absorption Fine Structure was carried out under the direction of N.D. Hutson (U.S. EPA). Database management and data presentation technical assistance was provided by L.H. Turner (Turner Technology, LLC) and P. Seignette (Energy Research Centre of the Netherlands). K. Ladwig and the Electric Power Research Institute (EPRI) are gratefully acknowledged for assistance in obtaining coal combustion residue samples and providing information from the EPRI database on coal combustion residues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".