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Record W4246557296 · doi:10.7287/peerj.preprints.2082

Silviculture approaches to restoring a predator-prey system: examples from the LiDea project in Boreal Alberta

2016· preprint· en· W4246557296 on OpenAlexaffabout
Michael Cody, Scott McNay, Glenn D. Sutherland, Geoff Sherman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsWoodland caribouSilvicultureHabitatBorealBogGeographyTaigaBlack spruceWoodlandRestoration ecologyEcologyEnvironmental scienceEnvironmental resource managementForestryPeatArchaeologyBiology

Abstract

fetched live from OpenAlex

The issue of Woodland caribou decline has been identified corporately as a top environmental priority for Cenovus energy Inc. Pursuant to this priority, a habitat centric environmental strategy and performance commitments have been developed. Beginning in 2008, Cenovus began applied investigation into the use of silviculture techniques for accelerated restoration, emphasizing the bog and fen forest site types that are characteristic of Boreal caribou habitat. In a larger scale project called LiDea, restoration treatments were ultimately applied to linear features throughout an area of 370 km 2 within the Cold Lake herd range. As indicated by metrics at the site level, as well as GPS collar re-locations, plant and animal response to restoration treatment are positive from a caribou perspective. Results from the LiDea series of projects have been strong enough to warrant the extension of these forest habitat restoration methods to the landscape scale.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.230
Teacher spread0.130 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes2
Has abstractyes

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