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
Introduced plant populations lose interactions with enemies, mutualists and competitors from their native ranges, and gain interactions with new species, under new abiotic conditions. From a biogeographical perspective, differences in the assemblage of interacting species, as well as in abiotic conditions, may explain the demographic success of the introduced plant populations relative to conspecifics in their native range. Within invaded communities, the new interactions and conditions experienced by the invader may influence both its demographic success and its effects on native biodiversity. Here, we examine indirect effects involving enemies, mutualists and competitors of introduced plants, and effects of abiotic conditions on biotic interactions. We then synthesize ideas building on Darwin's idea that the kinds of new interactions gained by an introduced population will depend on its relatedness to native populations. This yields a heuristic framework to explain how biotic interactions and abiotic conditions influence invader success. We conclude that species introductions generally alter plants' interactions with enemies, mutualists and competitors, and that there is increasing evidence that these altered interactions jointly influence the success of introduced populations.
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.
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