Book of Abstracts of the First Virtual GuardIAS Conference
Bibliographic record
Abstract
Oral Presenta onBuilding on recent advances in strengthening Argentina's national capacities to address biological invasions, including the approval of the Strategy on Invasive Alien Species in 2022, and the development of an early detection and rapid response (EDRR) protocol for marine invasive species in port areas, we are advancing non-native species data integration approaches to support effective biosecurity measures and assist in decision-making processes.To validate the EDRR protocol, we applied it following the early detection of three potentially invasive species in artificial wrecks, with focus on: (1) species identification through classical taxonomy and genetic analyses; (2) compilation of occurrence records, distribution data, knowed vectors, environmental tolerances and key biological traits to model potential distributions to identify high-risk areas and forecast future spread; and (3) assessment of regional and international connectivity through maritime traffic to evaluate potential introduction pathways; and (4) development of a biosecurity risk model based on port entry records from the maritime authority, thereby avoiding reliance on costly AIS-tracked data.These four components enabled us to estimate the invasion risk posed by commercial shipping traffic to Argentina's main marine ports, identify higher-risk routes, highlight the most vulnerable ports, and determine the ecoregions where new species introductions could occur.Our results provide valuable information to improve early detection strategies and risk assessment at seaports, complementing ongoing efforts to manage marine invasions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.120 |
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".