INVASIVESNET towards an International Association for Open Knowledge on Invasive Alien Species
Bibliographic record
Abstract
In a world where invasive alien species (IAS) are recognised as one of the major threats to biodiversity, leading scientists from five continents have come together to propose the concept of developing an international association for open knowledge and open data on IAS-termed "INVASIVESNET".This new association will facilitate greater understanding and improved management of invasive alien species (IAS) and biological invasions globally, by developing a sustainable network of networks for effective knowledge exchange.In addition to their inclusion in the CBD Strategic Plan for Biodiversity, the increasing ecological, social, cultural and economic impacts associated with IAS have driven the development of multiple legal instruments and policies.This increases the need for greater co-ordination, co-operation, and information exchange among scientists, management, the community of practice and the public.INVASIVESNET will be formed by linking new and existing networks of interested stakeholders including international and national expert working groups and initiatives, individual scientists, database managers, thematic open access journals, environmental agencies, practitioners, managers, industry, non-government organisations, citizens and educational bodies.The association will develop technical tools and cyberinfrastructure for the collection, management and dissemination of data and information on IAS; create an effective communication platform for global stakeholders; and promote coordination and collaboration through international meetings, workshops, education, training and outreach.To date, the sustainability of many strategic national and international initiatives on IAS have unfortunately been hampered by time-limited grants or funding cycles.Recognising that IAS initiatives need to be globally coordinated and ongoing, we aim to develop a sustainable knowledge sharing association to connect the outputs of IAS research and to inform the consequential management and societal challenges arising from IAS introductions.INVASIVESNET will provide a dynamic and enduring network of networks to ensure the continuity of connections among the IAS community of practice, science and management.
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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 | 0.085 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.028 | 0.021 |
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".