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Record W2115482532 · doi:10.1139/er-2014-0062

Can <i>Cylindrospermopsis raciborskii</i> invade the Baltic Sea?

2015· article· en· W2115482532 on OpenAlexvenueno aff
Jonna Engström‐Öst, Ivana Savatijević Rašić, Andreas Brutemark, Romi Rancken, Gordana Subakov Simić, Ane T. Laugen

Bibliographic record

VenueEnvironmental Reviews · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCylindrospermopsis raciborskiiHabitatFisheryAquacultureAlgal bloomEcologyInvasive speciesGeographyEnvironmental scienceBiologyFish <Actinopterygii>Phytoplankton

Abstract

fetched live from OpenAlex

Management actions against invasive species are usually most efficient during early stages of invasion. Monitoring for early detection is therefore part of many management plans. However, if monitoring efforts do not match suitable habitat areas, detecting the initial stages of an invasion may fail. We highlight this mismatch by assessing which areas have suitable habitats for an invasion of the cyanobacterium Cylindrospermopsis raciborskii in the Baltic Sea, and compare these with the areas that are currently monitored for algal blooms. Establishment of this potential toxin-producer in the Baltic Sea could have serious socio-economic consequences for tourism and recreation, as well as fisheries and aquaculture in the coastal regions. We estimate the coastal areas of the eastern Gulf of Finland as the most suitable area for establishment because of low salinity and high summer seawater surface temperatures. The species is not yet reported in the Baltic Sea, but in the suitable-habitat areas indicated by our assessment, very little monitoring is currently being done. We suggest several lines of research and monitoring to increase the probability of early detection and better predictions for the future distribution of the species.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.009

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 head0.024
GPT teacher head0.227
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations15
Published2015
Admission routes1
Has abstractyes

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