Early detection and rapid response: a cost-effective strategy for minimizing the establishment and spread of new and emerging invasive plants by global trade, travel and climate change.
Bibliographic record
Abstract
<title>Abstract</title> Over the past 50 years, considerable effort has been made by state and national agencies as well as other partners to minimize the establishment and spread of newly introduced and/or emerging invasive plants through single agency-led programmes, inter-agency councils and task forces and, most recently, the landscape approach to early detection and rapid response (EDRR). Examples of single agency-led programmes include the USDA-Carolinas Witchweed Eradication Program in the USA and the Kochia Eradication Project in Western Australia (EDRR 1.0). In recent years, state inter-agency councils and task forces have been formed to address all types of new invasive species - particularly newly introduced species that are not already regulated by federal or state agencies. The Delaware Invasive Species Council, the Ontario Invasive Plant Council and the Beach Vitex Task Force are good examples of this new trend in inter-agency partnering (EDRR 2.0). The landscape approach to EDRR involves the development of EDRR capacity at all levels of the landscape - local to national. It includes individual public and private land units, geographic land units (watersheds, biomes, corridors, etc.) and political land units (towns, counties, states/provinces and nations) (EDRR 3.0). From a societal standpoint, due to global climate change and increased global trade and travel, it is important to emphasize that the impacts of invasive species on food security, human health, and biodiversity will continue to increase unless steps are taken now to minimize their introduction, establishment and spread. Development of EDRR capacity at all levels of the landscape is a proven strategy for achieving those goals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".