Identifying invasive fish species threats to RAMSAR wetland sites in the Caspian Sea region—A case study of the Anzali Wetland Complex (Iran)
Bibliographic record
Abstract
Abstract Risk screening tools play a crucial role in identifying potential high‐risk non‐native (NN) fish species. In this study, potentially invasive NN fish species in the Anzali Wetland Complex (AWC), which is located on the south coast of the Caspian Sea (Iran), were identified using the Aquatic Species Invasiveness Screening Kit (AS‐ISK). Twenty‐nine freshwater fish species were screened of which 13 exist in the AWC and 16 in close proximity to it (“horizon” species). Receiver operating characteristic curve analysis showed that AS‐ISK could discriminate reliably between non‐invasive and invasive fish species for the AWC. Mean threshold scores were 3.25 for the Basic Risk Assessment (BRA) and 11.75 for the BRA + CCA (BRA + Climate Change Assessment), and these, respectively, classified 89.7% and 86.2% of the species as high risk. The CCA resulted in an increase in the BRA scores for 86.2% of the species, suggesting the need to account in future NN species management for a likely increased invasiveness of those species under future climate conditions. These results suggest that AS‐ISK could prove an effective tool for identifying potentially invasive NN freshwater fishes in other wetlands of the Caspian Sea basin.
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".