MétaCan
Menu
Back to cohort
Record W2214862618 · doi:10.7557/2.35.2.3636

Biodiversity offsets and caribou conservation in Alberta: opportunities and challenges

2015· article· en· W2214862618 on OpenAlexaffabout
Christine B. Robichaud, Kyle H. Knopff

Bibliographic record

VenueRangifer · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsWoodland caribouBiodiversityEnvironmental resource managementNatural resourceGeographyHabitatEnvironmental planningEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

The federal recovery strategy for boreal woodland caribou (Rangifer tarandus caribou) sets a goal of self-sustaining populations for all caribou ranges across Canada. All caribou herds in Alberta are currently designated as not self-sustaining and the recovery strategy requires an action plan to achieve self-sustaining status. At the same time, continued natural resource extraction in caribou ranges may be worth hundreds of billions of dollars. Some regulatory bodies have recognized an opportunity for biodiversity offsets to help meet the caribou recovery strategy’s goals while still permitting economic benefits of development. In this review, we evaluate offset opportunities for caribou in Alberta and practical impediments for implementation. We conclude that a number of actions to offset impacts of development and achieve no net loss or net positive impact for caribou are theoretically feasible (i.e., if implemented they should work), including habitat restoration and manipulations of the large mammal predator-prey system. However, implementation challenges are substantial and include a lack of mechanisms for setting aside some resources for long periods of time, public opposition to predator control, and uncertainty associated with loss-gain calculations. A framework and related policy for offsets are currently lacking in Alberta and their development is urgently needed to guide successful design and implementation of offsets for caribou.

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 score0.267
Threshold uncertainty score0.999

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 head0.101
GPT teacher head0.217
Teacher spread0.116 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueRangiferSame topicEnvironmental Conservation and ManagementFrench-language works237,207