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Record W7117315953 · doi:10.4236/gep.2025.1312024

Achieving the 30 by 30 Biodiversity Target in Canada through Indigenous Protected and Conserved Areas

2025· article· W7117315953 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Geoscience and Environment Protection · 2025
Typearticle
Language
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMitacs
KeywordsIndigenousBiodiversityEndangered speciesCorporate governanceHabitatHabitat conservationTraditional knowledgeMarine protected area

Abstract

fetched live from OpenAlex

Canada has committed to the United Nations Kunming-Montreal Global Biodiversity Framework (GBF), including protecting 30% of land and sea by 2030 while recognizing Indigenous rights. By 2024, Canada has conserved 13.8% of terrestrial and 15.5% of marine protected and conserved areas (PCAs), leaving a major gap in protection. This article posits that Indigenous protected and conserved areas (IPCAs) are the best way to protect an additional 160 million hectares of land and 80 million hectares of sea required to meet GBF target 3 by 2030 and fulfill UNDRIP commitments, and reconciliation promises. We explore this potential by mapping IPCAs against governance, critical habitats for species at risk, peatlands, and greenstone belts. Currently, Indigenous governance is underrepresented; of nearly 15,000 PCAs, only 96 (Rangifer tarandus caribou) and other endangered species.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.651
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.170
Teacher spread0.163 · 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