Влияние биологических инвазий на разнообразие и функционирование сообществ зоопланктона в эстуарных экосистемах Балтийского моря (обзор)
Bibliographic record
Abstract
Long-term study of zooplankton in the Neva Estuary ecosystem, one of the largest urbanized gulfs of the Baltic Sea, revealed the actuality of biodiversity investigations related to the increasing role of unintentional introduction of invasive species of planktonic organisms into the aquatic ecosystems. Peculiarities of plankton communities in the Baltic estuaries were analyzed in relation to the problem of biological pollution and present-day viewpoint on the paradox of brackish waters. It was shown that during 10 years since planktonic predator Cercopagis pengoi successfully invaded the eastern Gulf of Finland these crustaceans have not caused significant inhibitory effects on the high species diversity in this region of the Baltic Sea. The original method for the evaluation of the invaders' impact on zooplankton community elaborated by the author was verified using the data from Lake Ontario (North America). The study demonstrated the much stronger impact of Cercopagis on the aboriginal zooplankton community in Lake Ontario compared to the Neva Estuary ecosystem. Results of this investigation witness for the complexity of trophic interactions in the pelagic communities and specificity of their transformations under the impact of invasive species in aquatic ecosystems of different types.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".