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Record W3134195377 · doi:10.1086/713025

Biological Invasions and International Trade: Managing a Moving Target

2021· article· en· W3134195377 on OpenAlex
Rebecca S. Epanchin‐Niell, Carol McAusland, Andrew M. Liebhold, Paul Mwebaze, Michael Springborn

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Environmental Economics and Policy · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiosecurityUnintended consequencesPsychological interventionOrder (exchange)EconomicsInternational tradePolicy analysisPublic economicsBusinessEcologyPolitical scienceBiology

Abstract

fetched live from OpenAlex

International trade is a key pathway for the global spread of nonnative species. Historical and emerging trade flows interact with ecological dynamics to shape nonnative species risk and determine how that risk can be mitigated. This article discusses these underlying processes, emerging trade trends, and the role of past and future economics research in understanding and managing nonnative species risks from trade. We identify four priorities for future economics research. These include expanding economic analysis to consider interventions across the biosecurity continuum more comprehensively, leveraging new data systems for real-time prediction and effective allocation of inspection effort, applying economic analysis to anticipate and respond to emerging trade trends, and improving understanding of exporter and consumer behavioral responses to policy interventions in order to encourage intended (and ameliorate unintended) reactions.

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 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 score0.624
Threshold uncertainty score0.999

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)0.0020.000

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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