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Record W2023739592 · doi:10.2118/86686-ms

Reduction of Footprint and Restoration of Function: An Approach to Cumulative Effects Management

2004· article· en· W2023739592 on OpenAlexaffabout
Roger Creasey, Lisa Fischer

Bibliographic record

VenueSPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production · 2004
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsCumulative effectsFootprintPipeline (software)WildlifeDisturbance (geology)Environmental resource managementFunction (biology)Computer scienceEnvironmental scienceBusinessGeography

Abstract

fetched live from OpenAlex

Abstract The oil and gas industry tends to create numerous linear disturbances during the evolution of conventional field development. Access roads to wellsites, pipeline right-of-ways (ROWs), power lines, and seismic lines all contribute to other linear features created by forestry, mining, and public roads. Management of the resulting cumulative disturbance is difficult as there is usually a lack of land disturbance thresholds, holistic guidance from regulators, and individual companies can only manage their own operations and project design. Shell Canada is addressing its responsibility to control the contribution to this type of cumulative effect. This is being accomplished through improvements in the integration of project planning, reduction of the physical and effective ecological footprint of facilities, collaboration with other industries, and restoring the effectiveness of wildlife habitat. Where possible, an internal corporate policy of "no net increase" in linear developments is adopted. This paper will describe situations where Shell Canada is implementing practices to reduce the regional cumulative effects by managing its incremental disturbances on the land.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.399

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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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 designOther design
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

Citations2
Published2004
Admission routes2
Has abstractyes

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