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Record W4280635005 · doi:10.1142/s1464333222500168

Food Security Assessment: An Exploration of Canadian Offshore Petroleum SEA Practice

2021· article· en· W4280635005 on OpenAlexaffabout
Veronica Rohr, Jill Blakley, Phil Loring

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
Fundersnot available
KeywordsFood securityScope (computer science)Transparency (behavior)BusinessEnvironmental resource managementEnvironmental planningEnvironmental scienceEnvironmental economicsPolitical scienceGeographyComputer scienceComputer securityEconomicsAgriculture

Abstract

fetched live from OpenAlex

Strategic environmental assessment (SEA) has the potential to play a crucial role in addressing global food insecurity. This paper presents the results of an evaluation of 17 recent Canadian SEAs, conducted for offshore petroleum exploration, exploring the extent of consideration for food security in current SEA practice. Document analysis was used to appraise consideration of eight core food security elements and conformity to procedural and analytical elements recommended for effective food security assessment in regional SEA. Performance variation among the SEAs in was observed. Notable deficiencies include lack of explicit consideration for food security and lack of transparency around public participation, as well as limited characterisations of the socio-political environment. Some encouraging findings, however, suggest that food security can be successfully addressed in regional SEA. In particular, the ‘system analysis’ approach typically employed in SEA in the offshore petroleum exploration industry is well-suited to food security assessment. Certain aspects of food security are already indirectly considered and incorporated in SEA; yet, there is considerable scope for improvement of integrating food security effectively in SEA.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.003
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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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.

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

Citations18
Published2021
Admission routes2
Has abstractyes

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