MétaCan
Menu
Back to cohort
Record W2902928276 · doi:10.1007/s10530-018-1888-8

Biotic exchange from movement of ‘static’ maritime structures

2018· article· en· W2902928276 on OpenAlexafffund
Josephine C. Iacarella, Ian Davidson, Anya Dunham

Bibliographic record

VenueBiological Invasions · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans CanadaSmithsonian Institution
KeywordsTowingBiologyHabitatMarine ecosystemBiofoulingAnthropoceneEcosystemFisherySubmarine pipelineEcologyOceanographyMarine engineeringEngineering

Abstract

fetched live from OpenAlex

Coastal infrastructure provides extensive habitat for marine nonindigenous species (“NIS”), especially biofouling species. Ecosystem transplants can arise when static maritime structures (“SMS”) are relocated under slow towing speeds following long periods of biofouling accumulation. Translocations of SMS occur more often than acknowledged and represent a high-risk vector of marine NIS that is largely overlooked and unregulated. We compiled geolocations and movement patterns for oil platforms, derelict/obsolete vessels, barges, dry docks, floating lodges, homes and docks, aquaculture gear and buoys for the NE Pacific coast. We related these data to NIS richness within the region and to global examples of SMS movement. Our review and synthesis of SMS, paired with biofouling surveys before and after dock towing events, reveal the growing NIS vector opportunities resulting from sprawling coastal infrastructure. As coastal development continues apace in the Anthropocene, this emerging management challenge will require strong frameworks and workable solutions for a global issue that currently lacks incentives to prevent species transfers and invasions.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.956

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1140.001

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 head0.117
GPT teacher head0.250
Teacher spread0.133 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBiological InvasionsSame topicMarine Ecology and Invasive SpeciesFrench-language works237,207