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Record W2134872864 · doi:10.1196/annals.1439.007

Creating New Landscapes and Ecosystems

2008· review· en· W2134872864 on OpenAlexaffabout
Edward A. Johnson, Kiyoko Miyanishi

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2008
Typereview
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of GuelphUniversity of Calgary
Fundersnot available
KeywordsLand reclamationVegetation (pathology)Environmental scienceWetlandLandformHabitatOil sandsEcosystemEnvironmental resource managementWildlifeScale (ratio)Hydrology (agriculture)GeographyGeologyEcologyAsphaltGeomorphology

Abstract

fetched live from OpenAlex

Extraction of oil from the Alberta Oil Sands through surface mining involves the removal of the overburden and oil sand to a depth of up to 100 m and over extremely large areas. While the operation of the bitumen processing plants has serious environmental impacts on downstream habitats, this article focuses on the reclamation of areas from which the oil sands have been removed, processed, and returned. This reclamation following closure of the mines will entail the complete re-creation of landforms and ecosystems at a landscape scale, with the goal of producing suitable habitats for plants, animals, and people. Such projects will require a reasonable understanding of the geophysical and ecological processes that operate at a wide range of scales. Some information is provided on the climate, hydrology, vegetation, and land use (past and current) of the Oil Sands area, situated within the Boreal Plain ecozone, to provide a framework for discussion of issues to be addressed in, and proposed guidelines for, such large-scale reclamation. Although none of the mines has yet closed, numerous consultant reports have been produced with recommendations for various aspects of such reclamation projects (e.g., wetland hydrology, vegetation, wildlife habitat). The scientific basis of such reports is found to vary with respect to depth of understanding of the relevant processes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.811
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.336
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations118
Published2008
Admission routes2
Has abstractyes

Explore more

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