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Record W2119951909 · doi:10.3390/su2093110

Designing a Sustainable Future through Creation of North America’s only International Wildlife Refuge

2010· article· en· W2119951909 on OpenAlexaff
John H. Hartig, Rebecca S. Robinson, Michael A. Zarull

Bibliographic record

VenueSustainability · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsEnvironment and Climate Change Canada
FundersU.S. Fish and Wildlife Service
KeywordsBrownfieldSustainabilityAdaptive managementWildlife refugeEnvironmental resource managementEnvironmental planningRedevelopmentWildlifeStakeholderBusinessShoreProcess (computing)GeographyFisheryEngineeringEnvironmental scienceCivil engineeringComputer scienceEcologyManagementEconomics

Abstract

fetched live from OpenAlex

In 2001, the Detroit River International Wildlife Refuge was established based on the principles of conservation and sustainability. The refuge has grown from 49.1 ha in 2001 to over 2,300 ha in 2010. Agreement on a compelling vision for a sustainable future was necessary to rally stakeholders and move them forward together. Project examples include: lake sturgeon and common tern restoration; soft shoreline engineering; ecotourism; sustainable redevelopment of a brownfield; and indicator reporting. Key success factors include: a consensus long-term vision; a multi-stakeholder process that achieves cooperative learning; strong coupling of monitoring/research programs with management; implementing actions consistent with adaptive management; measuring and celebrating successes; quantifying benefits; building capacity; and developing the next generation of sustainability practitioners and entrepreneurs.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.003
Scholarly communication0.0050.005
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.009
GPT teacher head0.314
Teacher spread0.305 · 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 designQualitative
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
Published2010
Admission routes1
Has abstractyes

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