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Land Development in and around Protected Areas at the Wilderness Frontier

2012· article· en· W2141947191 on OpenAlexafffundabout
Shawn Leroux, Jeremy T. Kerr

Bibliographic record

VenueConservation Biology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Ottawa
FundersAgriculture and Agri-Food Canada
KeywordsProtected areaGeographyIUCN protected area categoriesWilderness areaWildernessFrontierLand useEnvironmental protectionLand developmentEnvironmental resource managementEnvironmental planningEcologyEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Protected areas' chief conservation objectives are to include species within their boundaries and protect them from negative external pressures. Many protected areas are not achieving these goals, perhaps in part due to land development inside and outside protected areas. We conducted spatial analyses to evaluate the ability of Canadian protected areas to mitigate the effects of nearby land development. We investigated correlations of national patterns of land development in and around protected areas and then examined national patterns of roads, urban area, and croplands in protected areas. We calculated the amount of developed land in protected areas and within 25-100 km of protected-area borders, the density of roads, and extent of urban and cropland area in protected areas. We constructed logistic-regression models to test whether development in a protected area was associated with landscape and protected-area characteristics. Land development was far less extensive inside than outside protected areas. However, several protected areas, particularly small southern areas near small urban centers had substantial development inside their boundaries, and nearly half of protected areas had roads. The cumulative extent of development within 50 km of protected areas was the best predictor of the probability of land development in protected areas. Canadian First Nations, industries, government, and nongovernmental organizations are currently planning an unprecedented number of new protected areas. Careful management of areas beyond protected-area boundaries may prove critical to meeting their long-term conservation objectives.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.404

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)0.0000.000

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 head0.017
GPT teacher head0.223
Teacher spread0.205 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2012
Admission routes3
Has abstractyes

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