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Record W1838256176 · doi:10.1139/a10-002

A conceptual framework for understanding, assessing, and mitigating ecological effects of forest roads

2010· article· en· W1838256176 on OpenAlexaffvenue
C. Robinson, Peter N. Duinker, Karen Beazley

Bibliographic record

VenueEnvironmental Reviews · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsConceptual frameworkEnvironmental resource managementEcologyEcosystemGeographyEnvironmental science

Abstract

fetched live from OpenAlex

A review of road-ecology literature suggests that impacts of forest roads on species and ecosystems begin during the road construction phase, but persist and accumulate well after a road is no longer in use. Over this time, impacts stemming originally from construction, but then also from the continued physical presence and human use of the road, follow complex multiple pathways ending in diminished species persistence. Yet in practice, road-impact considerations rarely extend beyond short-term issues related to road construction or beyond the spatial extent of the road corridor. Even when the range of potential impacts is recognized, managers rarely have a framework for assessing those impacts. This can be problematic, as informed decisions regarding the long-term, wide-ranging ecological consequences of road placement, design, and use can lessen the degree to which a road modifies the composition, structure, and function of forest ecosystems. This paper presents a conceptual framework for organizing, synthesizing, and applying our growing understanding of how roads affect forest ecosystems. The framework includes two parts: (1) a series of impact-hypothesis diagrams wherein ecological impacts are organized relevant to three phases of road existence: construction, presence and use; and (2) a five-step approach whereby ecological impact and road importance can be evaluated and a decision matrix used to determine appropriate mitigation strategies. Highlights of a case study conducted in southwestern Nova Scotia are presented to illustrate the applicability of the framework.

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 score0.224
Threshold uncertainty score0.866

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)0.0010.000

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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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

Citations64
Published2010
Admission routes2
Has abstractyes

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