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Record W2061718916 · doi:10.1038/npre.2007.689.1

Sustainability and Environmental Chemistry in Semi-Arid/Arid Regions: A Unique Research Opportunity with Global Implications

2007· preprint· en· W2061718916 on OpenAlexfundno aff
Sierra Rayne

Bibliographic record

VenueNature Precedings · 2007
Typepreprint
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsAridBiogeochemical cycleSustainabilityMultidisciplinary approachEnvironmental scienceEarth scienceEnvironmental resource managementEcologyChemistryEnvironmental chemistryGeologyBiologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.324
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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