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Record W1957117102 · doi:10.1139/cjfr-2014-0330

Reclamation of boreal forest after oil sands mining: anticipating novel challenges in novel environments

2014· article· en· W1957117102 on OpenAlexaffvenueabout
Patrick Audet, Bradley D. Pinno, Évelyne Thiffault

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité LavalNorthwestern PolytechnicNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsLand reclamationBorealEnvironmental scienceEcosystemTaigaEcologyLandformEnvironmental resource managementGeography

Abstract

fetched live from OpenAlex

Boreal forests in northern Alberta have a growing anthropogenic footprint due to a rapidly growing oil sands mining industry. Although land reclamation is a necessary aspect of responsible industrial development, these activities nearly always affect higher order landscape components such as the broader landform, and its hydrology and biogeochemistry. Recent anthropogenic impacts are then believed to result in new environmental conditions and obstacles under which the boreal forest is developing, potentially leading to irreversibly different environments that could be characterized as novel ecosystems. Reflecting an emerging trend across the field of restoration ecology, these novel ecosystems are not necessarily undesirable. Instead, they are an unavoidable consequence of pervading anthropogenic effects on natural ecosystems. It is our view that successful reclamation outcomes can still be derived so long as policy and regulatory requirements are afforded the necessary scope and economic flexibility to account for the development of hybrid and novel ecosystems among highly disturbed mine sites. Hence, this analysis seeks to situate current and anticipated challenges affecting the reclamation of boreal forest following oil sands mining by describing (i) how regulatory criteria shape reclamation practices and targeted end goals and (ii) how these approaches embody latest trends and priorities in the area of restoration ecology.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.061
GPT teacher head0.291
Teacher spread0.229 · 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 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

Citations73
Published2014
Admission routes3
Has abstractyes

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