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Record W2117565823 · doi:10.1061/9780784413067.013

Endangered Species Act and Marine Mammal Protection Act Permitting in the Pacific Northwest

2013· article· en· W2117565823 on OpenAlexfundno aff
Tabitha Reeder, Brian Carrico, Dan Gunderson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersU.S. Fish and Wildlife ServiceUniversity of Waterloo
KeywordsWork (physics)ScheduleEndangered speciesDocumentationPort (circuit theory)Environmental resource managementEnvironmental protectionEnvironmental planningEnvironmental scienceBusinessEngineeringComputer scienceEcologyHabitat

Abstract

fetched live from OpenAlex

The construction, maintenance, and repair of critical port infrastructure in the marine and freshwater environments of the Pacific Northwest frequently requires in-water and/or overwater work, which triggers specific requirements for federal environmental permitting and documentation. The Endangered Species Act (ESA) and the Marine Mammal Protection Act (MMPA) are the two federal regulations that generally have the greatest potential effect on project design, cost, and schedule. This paper discusses the current regulatory climate surrounding ESA and MMPA compliance by comparing the impacts of these regulations on two recently permitted projects. The projects are located at sites within the Puget Sound and on the Lower Columbia River, and range from routine activities, such as pile repair and replacement, to the construction of new marine terminals. The paper presents the design requirements for the projects, the way in which the project addressed ESA and MMPA compliance, and how the compliance process affected the project design, cost, and/or schedule. The comparative analysis concludes with a summary of the potential effects to project schedule, design, and cost, and a discussion of the ways in which project managers, design teams, engineers, and environmental staff can work to minimize these potential issues.

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 score0.284
Threshold uncertainty score0.389

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 head0.009
GPT teacher head0.177
Teacher spread0.168 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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