Guidelines for Climate-Smart Invasive Species Management
Bibliographic record
Abstract
Climate change and invasive species pose novel and combined challenges to ecosystem management and ecological restoration. Managers and decision-makers can address these challenges via climate-smart invasive species management, defined as any management strategy or action that considers and aims to reduce the interactive effects of climate change and invasions. To facilitate this approach in the Northeastern U.S. and Canada, members of the Northeast Regional Invasive Species & Climate Change Management Network (NE RISCC) have created a set of guidelines for how to consider and incorporate the interactive effects of climate change and invasions at multiple stages of management based on feedback from managers via surveys, formal research interviews, informal conversations, a workshop at the 2023 New York Invasive Species Expo, and an online workshop in April 2024. The focus of these guidelines is on the areas served by the NE RISCC, but can also serve as a starting point for climate-smart invasive species management efforts in other regions.
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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 | 0.032 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".