An Analysis of Invasive Species Management in the Niagara Region of Ontario, Canada: Establishment of a Database to Improve Knowledge Sharing
Bibliographic record
Abstract
Worldwide, 17,000 invasive species drive biodiversity loss, and cost the global economy at least $1.4 trillion annually. The UN and Convention on Biological Diversity have declared invasive species a global initiative and requested increased data sharing on invasives. Existing worldwide and local databases focus on distribution, abundance, identification, and impacts of invasives, but not management activities. No database focuses on invasive management for the Niagara Region of Ontario, Canada. This study used sustainability science and the Ecosystem Approach Principles to guide the design of an invasive species management database. The goal of the study was to document current aquatic and riparian invasive management activities in the Niagara Region and develop a database that would become a tool to facilitate collaboration at the regional level. The objectives were to (1) inventory current invasive detection and control activities in the Niagara Region and make comparisons to recommended techniques in the literature; (2) examine perceived efficacy of control techniques; and (3) develop a database integrated with a GIS mapping component. Seventy-one organizations involved in riparian/aquatic invasive management in the Niagara Region were contacted and 16 were interviewed in-depth. In 2017/2018 there were 35 separate control efforts reported, involving 10 riparian invasives and two aquatic invasives, with most concentrated along the Niagara River. Collaboration efforts were minimal, occurring for only six specific projects. Recommendations from this study include: develop a regional invasive species plan; increase control efforts along the Welland Canal and Lake Erie shoreline; consider a wider variety of control techniques; and increase collaboration, information-sharing and resource-sharing among organizations. Overall, this database provides a baseline for the current state of aquatic and riparian invasive management activities in the Region, and can be used as a tool to identify resource-sharing opportunities, management efficacy, priority areas, areas of improvement, and future resource needs. This can help Niagara progress towards achieving Aichi Biodiversity Target 9 and the United Nation’s Sustainable Development Goal Target 15.8.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".