Spatial and Temporal Distribution of Cyanobacteria and Their Relationship with Environmental Parameters In The Aby System Lagoon (south-eastern Ivory Coast, West Africa)
Bibliographic record
Abstract
Cyanobacteria are often significant components floating microphyte communities, contributing to the biological diversity and, in some cases, providing most of the carbon sources that sustain aquatic food webs (Seu-Anoï, 2012).In terms of morphology, physiology and metabolism, Cyanobacteria (blue-green algae) are one of the most diverse groups of gram-negative photosynthetic prokaryotes (Singh et al., 2011).Cyanobacteria have an important role in aquatic ecosystems and they make up part of the planktic, metaphytic, or benthic communities, representing the base of trophic chain; they are responsible for part of primary productivity of aquatic systems and are relevant in biogeochemical cycles (Seu-Anoï et al., 2017;Grzesik et al., 2017).Moreover, some species of Cyanobacteria have ability to incorporate nitrogen.Indeed, Cylindrospermopsis raciborskii has the capacity to fix atmospheric nitrogen (N2) through heterocytes, as do other Nostocales, conferring a competitive advantage in nitrogen-depleted environments relative to P. agardhii, which cannot fix nitrogen (Whitton and Potts, 2000).Due to their capacity for photosynthesis, Cyanobacteria can rapidly become dominant in aquatic and terrestrial habitats by forming intensive blooms.The development of cyanobacterial blooms has become a serious problem in recent decades, because many bloom-forming species are reported to be able to produce secondary metabolites toxic to many organisms, including humans (Bollina et al., 2012).Temperature increases in the range of 0.2 °C per decade, and their effects on water mixing regimes, are expected to increase the occurrence, frequency and duration of cyanobacterial blooms in several regions of the planet (Markensten et al., 2010).These future changes in climate are also predicted to cause shifts in the species composition of cyanobacterial blooms in favour of invasive species (Mehnert et al., 2010).These can have a strong negative effect on water quality, as certain species of Cyanobacteria are capable of producing toxins.Increased dominance of cyanobacteria on coastal lagoon is a continuing phenomenon in near shore tropical environments, particularly in areas impacted by humans (Béginet al., 2016).In the Aby Lagoon, northern hemisphere summer blooms of Cyanobacteria have been observed (Seu-Anoï, 2012); they usually are dominated by 3-4points
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".