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Record W2907447614 · doi:10.1201/9780203733882-16

Hydrogeological Criteria for Buffer Zones Between Wetlands and Aggregate Extraction Sites

2017· book-chapter· en· W2907447614 on OpenAlexaboutno aff
J. Z. Fraser, D. Routly, A. B. F. Hinton, K A Richardson

Bibliographic record

VenueWetlands · 2017
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogeologyExtraction (chemistry)WetlandBuffer (optical fiber)Aggregate (composite)Environmental scienceHydrology (agriculture)GeologyGeotechnical engineeringEngineeringChemistryChromatographyMaterials scienceEcologyBiology

Abstract

fetched live from OpenAlex

Although pits and quarries have been established and operated in close proximity to wetlands throughout southern Ontario, the nature and extent of their impacts on wetland processes and functions are not well documented. A better understanding of these impacts on local hydrogeological processes and wetland functions and on the environmental gradients which define wetland boundaries is required to effectively implement Ministry of Natural Resources wetlands and aggregate resource management policies. The Southern Region Science and Technology Transfer Unit of the Ministry of Natural Resources is currently assessing the range and scale of these impacts to: identify situations where significant impacts may occur; improve siting criteria and define buffer zones; identify operating practices which mitigate negative impacts; maintain local hydrogeological processes; and to develop rehabilitation strategies for the long-term protection and enhancement of adjacent wetlands. This effort is part of long-term monitoring and experimental management initiatives which will be aimed at improving our understanding of wetland gradients, boundaries, and buffer zones in relation to aggregate extraction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.279
Teacher spread0.221 · 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
Published2017
Admission routes1
Has abstractyes

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