Stoichiometry and planktonic communities structure in littoral and pelagic zones of two lakes with different trophic types in Belarus
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".