An Introduction to the Effect of Heterogeneities on the Characterization and Remediation of Alluvial Geosystems
Bibliographic record
Abstract
Research Article| January 01, 2003 An Introduction to the Effect of Heterogeneities on the Characterization and Remediation of Alluvial Geosystems R. E. JACKSON R. E. JACKSON 1INTERA Inc., 9111A Research Blvd., Austin, TX 78758 Search for other works by this author on: GSW Google Scholar Environmental & Engineering Geoscience (2003) 9 (1): 1–4. https://doi.org/10.2113/9.1.1 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation R. E. JACKSON; An Introduction to the Effect of Heterogeneities on the Characterization and Remediation of Alluvial Geosystems. Environmental & Engineering Geoscience 2003;; 9 (1): 1–4. doi: https://doi.org/10.2113/9.1.1 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Heterogeneous granular sediments—sands and gravels —form the most important water-supply aquifers in many areas of North America. In the past 60 years many such aquifers have become contaminated with non-aqueous phase liquids (NAPLs) and the dissolved phases associated with them. These include glacial outwash deposits in New England, Ohio, and Ontario, the floodplain alluvium along the Columbia and Fraser rivers, the buried channel aquifers beneath the U.S. Midwest and the Canadian prairies, and the huge coalescing alluvial-fan aquifers of the U.S. Southwest basin-and-range province. These heterogeneous granular sediments are referred to in this article as ‘alluvial geosystems' when contaminated by... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.034 |
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".