Bibliographic record
Abstract
With nearly ten years of experience, Golder Associates Inc. (Golder) is a leader in the manufacture and implementation of nano-scale zero-valent iron (nZVI) for environmental remediation applications under licensing agreement with Lehigh University. Golder has designed and implemented nZVI injections in the United States, Canada and across Europe at twenty sites, either as the lead consultant or in partnership with Universities and other contractors. In addition, the United States Environmental Protection Agency (USEPA) and state regulatory agencies have participated actively in providing comments and feed-back on proposed nZVI injections, resulting in the further development and understanding of this maturing technology. Golder's global experience has led to realization of the state-of- the-technology including: determination of the importance of a well-developed Site Conceptual Model (SCM); verification of the need to include surface modifiers to enhance the mobility of nZVI in the subsurface; verification of the need to include a catalyst for in situ treatment using mechanically crushed material; and, determination of the enhanced treatment potential from combination nZVI/enhanced bioremediation applications. The following chapter expands on these advancements and looks forward to the future needs of this maturing technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".