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Record W1570911571

Simulating enforcement and compliance as part of an evaluation framework for ballast water regulations

2007· dissertation· en· W1570911571 on OpenAlexfundno aff
Tyler Vincent Gray, Randall M. Peterman

Bibliographic record

VenueSummit (Simon Fraser University) · 2007
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsBallastEnforcementCompliance (psychology)Clean Water ActBusinessRisk analysis (engineering)Environmental scienceEngineeringEcologyWater qualityBiology
DOInot available

Abstract

fetched live from OpenAlex

There is evidence that even if ballast water exchange is mandatory, without strong enforcement, compliance with regulations may be significantly lower than that reported by vessels. Meaningful evaluation of ballast water regulations should therefore take into account predicted violation rates in response to enforcement. To address this issue, I present a framework for simulating the violation-enforcement relationship, in which the output is the proportion of vessels that violate ballast water regulations. I apply the simulated violation results to a simple dose-response model to evaluate the relative importance of the relationship between violation rate and invasion rate. Results demonstrate that the benefit of improving (or even accurately quantifying) the violation rate is highly dependent on the biological uncertainty in the relationship between ballast water and establishment of invasive species. As for minimizing the violation rate, the best allocation of an enforcement budget is robust to the model assumptions explored here.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.994

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)0.0060.000

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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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