Sustainability and Environmental Chemistry in Semi-Arid/Arid Regions: A Unique Research Opportunity with Global Implications
Bibliographic record
Abstract
Abstract Dr. Sierra Rayne will be speaking on the interplay of sustainability and environmental chemistry in semi-arid and arid regions worldwide. Drawing on his previous, current, and proposed research on organic and inorganic contaminants in aquatic systems, Dr. Rayne will illustrate the importance of multidisciplinary and interdisciplinary approaches towards tackling environmental problems. A key element of his work is looking at chemical dynamics in environmental matrices, and in particular, photochemically generated reactive intermediates and their impact on biological systems and net ecosystem functions. Semi-arid/arid regions also offer unique opportunities to focus on the role of photochemistry in the biogeochemical cycling of oxyanion-forming heavy metals such as arsenic, molybdenum, selenium, and uranium (among others). Given the importance of semi-arid/arid regions in hosting major mineral deposits, multidisciplinary environmental chemistry research can also help make contributions towards sustainability in the worldwide mining industry. These fields offer great opportunities for researchers and students interested in semi-arid/arid landscapes, and understanding the role and impact of these regions on global contaminant fluxes is at the core of Dr. Rayne’s program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".