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Record W2527786644 · doi:10.1177/194008291500800308

Robots as Vectors for Marine Invasions: Best Practices for Minimizing Transmission of Invasive Species Via Observation-Class ROVs

2015· article· en· W2527786644 on OpenAlex

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

VenueTropical Conservation Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsNautilus Environmental
Fundersnot available
KeywordsRemotely operated underwater vehicleRemotely operated vehicleInvasive speciesEcosystemMarine ecosystemTransmission (telecommunications)FisheryEnvironmental scienceComputer scienceRobotEcologyOceanographyBiologyMobile robotArtificial intelligenceTelecommunicationsGeology

Abstract

fetched live from OpenAlex

Remotely operated vehicles (ROVs) present a potential risk for the transmission of invasive species. This is particularly the case for small, low-cost microROVs that can be easily transported among ecosystems and, if not properly cleaned and treated, may introduce novel species into new regions. Here we present a set of 5 best-practice guidelines to reduce the risk of marine invasive species introduction for microROV operators. These guidelines include: educating ROV users about the causes and potential harm of species invasion; visually inspecting ROVs prior to and at the conclusion of each dive; rinsing ROVs in sterile freshwater following each dive; washing ROVs in a mild bleach (or other sanitizing agent) solution before moving between discrete geographic regions or ecosystems; and minimizing transport between ecosystems. We also provide a checklist that microROV users can incorporate into their pre- and post-dive maintenance routine.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.176
GPT teacher head0.329
Teacher spread0.153 · 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