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Stoichiometry and planktonic communities structure in littoral and pelagic zones of two lakes with different trophic types in Belarus

2023· article· en· W4313433211 on OpenAlexaff
Zhanna Buseva, Sh. G. Farahani, Vladimir Razlutskij, E. A. Sysova, Natallia Maisak, K. V. Myagkova, Paul C. Frost

Bibliographic record

VenueDoklady of the National Academy of Sciences of Belarus · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsTrent University
FundersBelarusian Republican Foundation for Fundamental Research
KeywordsPelagic zoneSestonLittoral zoneTrophic levelZooplanktonLake ecosystemPlanktonEnvironmental scienceEcologyOceanographyPhytoplanktonEcosystemBiologyNutrientGeology

Abstract

fetched live from OpenAlex

Stoichiometric C : N : P ratios were compared between primary producers in littoral and pelagic ecosystems of mesotrophic relatively shallow lake Obsterno and shallow macrophyte covered low trophic lake Nobisto from May to October over the next two years. Elemental seston ratios of lake Obsterno revealed smaller differences between littoral and pelagic zones in comparison with lake Nobisto in 2017. During the studied period, in the both lakes, the seston C : N and C : P ratios were higher than the Redfield ratio (106 : 16 : 1 C : N : P) on most dates and N : P was always more than 16. Pelagic C : P and N : P ratios in lake Obsterno were the highest in May in 2017, July and September in 2018 with significant differences between littoral and pelagic zones. N : P ratios decreased in October but there were no significant differences among habitats. In lake Nobisto in 2018, seston C : P and N : P ratios increased from May to July in littoral and pelagic zones but then decreased in September to October. Our research shows differences in stoichiometric ratios in littoral and pelagic zones of these two lakes, which indicates food quality (seston C : N : P ratios) differences for zooplankton species depending on season and location.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

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