Bibliographic record
Abstract
Humanity’s rise is rapidly moulding the structure and functioning of the biosphere over the surface of our planet, while human-mediated translocations of organisms – an inevitable consequence of this rise – is driving further transformation (Pyšek et al. 2020b). Drawing inspiration and concepts from population ecology, Invasion Science 1.0 (see Chapter 1) has explored the myriad ways a focal alien species can negotiate geographical, ecological and environmental barriers to establish and potentially invade in new novel environments. Coordinated efforts have been made to classify introduction pathways (Hulme et al. 2008; Wilson et al. 2009); forecast invasion risks (Kumschick and Richardson 2013) and impacts (Jeschke et al. 2014); model invasive spread (Hui and Richardson 2017); unify invasion frameworks (Wilson et al. 2020); and prescribe management strategies such as early detection and rapid response to prevent, contain and eradicate problematic species (Wilson et al. 2017). However, the phenomenon of biological invasions involves all types of organisms, ecosystems and a wide range of contexts and framings; this has given rise to a plethora of invasion hypotheses and theories that seek to explain and ultimately predict aspects of invasion dynamics and the expected outcome of specific management actions (Jeschke and Heger 2018). Most invasion hypotheses are relevant in specific contexts and often fail when faced with the reality of contextual complexity. This has led to a wave of syntheses that have attempted to classify invasion cases and hypotheses based largely on three aspects – invasive traits, site characteristics and invasion pathways (Pyšek et al. 2020a). To embrace considerations that arise when attempting to merge insights from all these perspectives, a paradigm shift began emerging at the turn of the millennium, together with the rise of network science. It embraces the complexity of biotic interaction networks (Figure 7.1; e.g., Segar et al. 2020), the trait paradigm in community ecology (Figure 7.2; e.g., McGill et al. 2006; Salguero-Gómez et al. 2018), and considers how functional traits of species dictate their ecology and roles in networks (Figure 7.3; e.g., Mello et al. 2019). This new lens for drawing together threads pertaining to all facets of biological invasions (Invasion Science 2.0) seeks to elucidate the structure and function of an ecological network facing biological invasions. This book has laid out a road map of signposts, hazard warnings and shortcuts for the journey to Invasion Science 2.0, framing and classifying research topics and offering tentative solutions and travel advisories.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".