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Record W4394853014 · doi:10.1111/rec.14152

Bionovelty and ecological restoration

2024· article· en· W4394853014 on OpenAlexafffund
John P. Volpe, Eric Higgs, Jonathan M. Jeschke, Katie Barnhill, Conrad G. Brunk, Joan Dudney, Laura L. Govers, Richard J. Hobbs, Karen Keenleyside, Stephen D. Murphy, Philip J. Seddon, Jayce Sudweeks, Orkan Telhan, Sonia A. Voicescu

Bibliographic record

VenueRestoration Ecology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcosystem dynamics and resilience
Canadian institutionsUniversity of WaterlooUniversity of Victoria
FundersGenome British Columbia
KeywordsSoftware deploymentRestoration ecologyVariety (cybernetics)NoveltyNovel ecosystemEnvironmental resource managementEcologyEcosystemEnvironmental restorationEcosystem servicesEnvironmental scienceComputer scienceBiology

Abstract

fetched live from OpenAlex

Anthropogenic activity has irreparably altered the ecological fabric of Earth. The emergence of ecological novelty from diverse drivers of change is an increasingly challenging dimension of ecosystem restoration. At the same time, the restorationist's tool kit continues to grow, including a variety of powerful and increasingly prevalent technologies. Thus, ecosystem restoration finds itself at the center of intersecting challenges. How should we respond to increasingly common emergence of environmental system states with little or no historical precedent, whilst considering the appropriate deployment of potentially consequential and largely untested interventions that may give rise to organisms, system states, and/or processes that are likewise without historical precedent? We use the term bionovelty to encapsulate these intersecting themes and examine the implications of bionovelty for ecological restoration.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.040
Scholarly communication0.0070.008
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations18
Published2024
Admission routes2
Has abstractyes

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