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Record W2129128614 · doi:10.1890/110267

The changing role of history in restoration ecology

2014· review· en· W2129128614 on OpenAlexafffund
Eric Higgs, Donald A. Falk, Anita Guerrini, Marcus Hall, Richard J. Hobbs, Stephen T. Jackson, Jeanine M. Rhemtulla, William Throop

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMcGill UniversityUniversity of Victoria
FundersPacific Institute for Climate SolutionsOregon State University
KeywordsRestoration ecologyEcologyFidelityHistorical ecologyInterpretation (philosophy)Environmental ethicsFace (sociological concept)Environmental resource managementSociologyBiologySocial scienceEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

In the face of rapid environmental and cultural change, orthodox concepts in restoration ecology such as historical fidelity are being challenged. Here we re‐examine the diverse roles played by historical knowledge in restoration, and argue that these roles remain vitally important. As such, historical knowledge will be critical in shaping restoration ecology in the future. Perhaps the most crucial role in shifting from the present version of restoration ecology (“v1.0”) to a newer formulation (“v2.0”) is the value of historical knowledge in guiding scientific interpretation, recognizing key ecological legacies, and influencing the choices available to practitioners of ecosystem intervention under conditions of open‐ended and rapid change.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.009
Scholarly communication0.0050.009
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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 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

Citations415
Published2014
Admission routes2
Has abstractyes

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