A decision framework for the management of established biological invasions
Bibliographic record
Abstract
In some cases, managing an established invasive species may do more harm to an ecosystem than allowing the invader to persist. Given limited resources available to land managers and the realities of conservation triage, we recognized the need for systematic guidance for management decisions made at the “late end” of the invasion curve. We gathered an interdisciplinary group of experts and practitioners to address the question of “under what circumstances is the active management of an established aquatic invasive species warranted?” Our working group identified three key dimensions to this question: (1) the efficacy of available management options; (2) the net benefits of management actions weighed against the null scenario of no control; and (3) the socio-ecological context that defines management goals, a manager’s ability to achieve said goals, and perceptions of management outcomes. These considerations were used to structure a consensus decision tree that supports a multi-criteria approach to decision-making. Our approach promotes interdisciplinarity and systems thinking and emphasizes the need to consider costs and benefits comprehensively, for example by considering the persistence or reversibility of impacts from both the invasive species and from efforts to suppress or eradicate it.
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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".