Indicators for the multiscale management of biological invasions applied to Aquarana catesbeiana in Uruguay
Bibliographic record
Abstract
Background. Uruguay still does not have indicators to manage biological invasions, of which only one, the invasive alien species Aquarana catesbeiana (bullfrog), could be eradicable. Objectives. To identify and determine indicators, aligned with target 6 of the Kunming-Montreal Global Biodiversity Framework (Convention on Biological Diversity) and Sustainable Development Goal 15.8 to improve the governance of bioinvasions. Methods. Data at different scales available on open information platforms were used to calculate indicators of the drivers/pathways-pressure-state-response (IPER) model. Results. A system of 8 indicators for the management of bioinvasions in Uruguay is presented: 1) introduction pathways, maps of prioritized IAS; 2) No. of documented IAS records (86), 3) IAS list (42); 4) No. of IAS in protected areas (53); 5) No prioritized IAS (8), 6) Species threatened by IAS; 7) SDG indicator 15.8.1 and 8) No. of plans in execution for prioritized IAS. For the bullfrog, indicators were calculated at the country level (4) and at the local level (4); country scale: 1) Introduction pathways: No. of frog farms (23); 2) No. of invaded water bodies (71, in 3 of 19 departments); 3) IAS that threaten native biodiversity (A. catesbeiana); and 4) SDG 15.8.1; local scale (Canelones): 1) No. of frog ponds (2); 2) No. of invaded ponds (7); 3) No. of invaded ponds prioritized (3); and 4) No. of control plans (1). Conclusions. An established system of indicators and a supporting database available online is identified for Uruguay. It is recommended to apply the IPER conceptual framework to other IAS and at different spatial scales to contribute to the protection of biodiversity against bioinvasions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".