MétaCan
Menu
Back to cohort
Record W2170620188 · doi:10.1139/f02-088

Patterns and mechanisms of aquatic invertebrate introductions in the Ponto-Caspian region

2002· article· en· W2170620188 on OpenAlexvenueno aff
Igor A. Grigorovich, Hugh J. MacIsaac, Edward L. Mills

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
Fundersnot available
KeywordsInvertebrateBiological dispersalEcologyFaunaBiologyHabitatMarine invertebratesIntroduced speciesEstuaryMediterranean climateFisheryPopulation

Abstract

fetched live from OpenAlex

The Black, Azov, and Caspian sea drainages (i.e., Ponto-Caspian region) have an extensive and long history of species introductions. Here we review patterns and mechanisms of introductions of aquatic invertebrate species into these ecosystems. Since the late 1800s, 136 free-living and 27 parasitic invertebrate species have been introduced outside their native ranges and have established reproducing populations in the Ponto-Caspian region. The bulk of these introductions are represented by crustaceans (53%), flatworms (15%), and molluscs (13%). Most of the introduced species are native to other areas within the Ponto-Caspian region (37%), with other sizable contributions from the Atlantic–Mediterranean (15%) and boreal European–Siberian (14%) geographic regions. Mechanisms of introductions were dominated by deliberate releases (29%) and shipping activities (22%), with the former occurring principally in freshwater habitats and the latter in marine and estuarine ones. Other introductions resulted from unintentional release (21%) and hydrotechnical development (14%), notably the construction of reservoirs and canals. Global and regional trade, particularly that mediated by commercial ships, provides dispersal opportunities for nonindigenous invertebrates to and within the Ponto-Caspian region, rapidly changing the composition of its endemic fauna.

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 score0.249
Threshold uncertainty score1.000

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)0.0010.000

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 head0.023
GPT teacher head0.190
Teacher spread0.167 · 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

Citations90
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicMarine Ecology and Invasive SpeciesFrench-language works237,207