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Record W2950286127 · doi:10.1111/rec.12994

Do or do not. There is no try in restoration ecology

2019· article· en· W2950286127 on OpenAlexaff
Christopher J. Lortie, Julie St. John, Will Spangler

Bibliographic record

VenueRestoration Ecology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsYork University
Fundersnot available
KeywordsRestoration ecologyEcologyValue (mathematics)Field (mathematics)Process (computing)PerceptionEnvironmental ethicsWork (physics)SociologyEnvironmental resource managementPsychologyComputer scienceBiologyEngineeringEnvironmental science

Abstract

fetched live from OpenAlex

Change is a fundamental component of contemporary restoration ecology. The environment, the research, and the ideas in this discipline are rapidly evolving and changing. The California Society for Ecological Restoration annual meeting was an inclusive, diverse meeting that significantly advanced new thinking in the field and provided an exemplar of the value of scientific discourse at meetings. The restoration work in this region also amplified and identified trends in the scientific community at large. A total of three future‐oriented strategic issues emerged from the discourse at this meeting. (1) Restoration ecologists need to consider alternative definitions of local for interventions within a region. (2) Restoration is never complete and must always incorporate people. (3) Indirect outcomes and the process of restoration have merit despite challenges of immediate identification of benefits. The science presented served as a platform for these advanced strategic issue examinations, and the grandest of challenges for restoration ecology necessarily includes people in every equation and embraces values and perceptions over longer time frames.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0200.018

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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