Introducing AlienScenarios: a project to develop scenarios and models of biological invasions for the 21 st century
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AlienScenarios, a three-year project starting in March 2019, will evaluate for the first time the range of plausible futures of biological invasions for the 21 st century. AlienScenarios consists of seven project partners and seven integrated complementary subprojects. We will develop the qualitative narratives for plausible futures of global alien species richness and impacts in the 21 st century – the Alien Species Narratives (ASNs). The ASNs further serve as overarching concept to parameterize quantitative models of global, continental and regional futures of biological invasions. We will also establish the first global mechanistic invasion model considering major processes of biological invasions such as source pools, driver dynamics and establishment rates. Further, we will assess the impacts of invasive alien species (IAS) in terms of economic costs according to the different ASNs. In addition, we will assess the consequences of different levels of implementation of the European Union Regulation on IAS. Providing some more detailed regional information, we will analyse changes of the functional composition of communities in mountain regions under different scenario storylines and will extend the analyses to the Global South using Panama as a country-level case study. Finally, the results of the other WPs will be synthesized, and the approach and results of AlienScenarios will be discussed with and communicated to stakeholders and the wider community. AlienScenarios will provide crucially needed insights for pro-active alien species management and policy. It will thus make an important contribution to global assessments and projections of biodiversity and ecosystem services, as well as regional policies (e.g. EU regulation on IAS).
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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 it