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Record W2007631985 · doi:10.3390/land3010167

Development by Design in Western Australia: Overcoming Offset Obstacles

2014· article· en· W2007631985 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.
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

VenueLand · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsnot available
FundersBarrick Gold Corporation
KeywordsEnvironmental resource managementEnvironmental planningBiodiversityEnvironmental impact assessmentBusinessOffset (computer science)GeographyEnvironmental scienceComputer scienceEcology

Abstract

fetched live from OpenAlex

Biodiversity offsets can be an important tool for maintaining or enhancing environmental values in situations where development is sought despite negative environmental impacts. There are now approximately 45 compensatory mitigation programs for biodiversity impacts worldwide, with another 27 programs in development. While offsets have great potential as a conservation tool, their establishment requires overcoming a number of conceptual and methodological hurdles. In Australia, new policy changes at the national and state (i.e., Western Australia) level require that offsets follow a set of general principles: (1) Environmental offsets may not be appropriate for all projects and will only be considered after avoidance and mitigation options have been pursued; (2) Environmental offsets will be based on sound environmental information and knowledge; (3) Establishing goals for offsets requires an estimate of expected direct and indirect impacts; (4) Environmental offsets will be focused on longer term strategic outcomes; (5) Environmental offsets will be cost-effective, as well as relevant and proportionate to the significance of the environmental value being impacted. Here we focus on the challenges of determining and implementing offsets using a real world example from a voluntary offset process undertaken for Barrick Gold’s Kanowna Belle mine site in Western Australia to highlight those challenges and potential solutions.

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)0.000
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 score0.401
Threshold uncertainty score0.788

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)0.0010.001

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 head0.032
GPT teacher head0.236
Teacher spread0.204 · 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