Ascidians as models for studying invasion success
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
During the past three decades, coastal marine waters have become among the most invaded habitats globally. Ascidians are among the most notorious invaders in these ecosystems. Owing to their rapid spread, frequent population outbreaks, and associated negative ecological and economic impacts, invasive ascidians have become a global problem. Thus, the study of ascidian invasions has become a prominent area of invasion biology. Here, we review current knowledge and conclude that ascidians are good models for studying invasion success in the marine realm. Firstly, we summarize the reconstruction of invasion pathways or colonization histories and associated negative impacts of invasive ascidians, and address the urgent need to clarify ambiguous taxonomy of ascidians. Secondly, we discuss factors that underlie or facilitate invasion success of ascidians, including vectors of introduction and spread, environmental changes, biological traits, and possible genetic issues. Finally, we summarize current science-based policies and management solutions that are in place to prevent and control spread of invasive ascidians. We conclude by highlighting key research questions that remain to be answered, and propose future research to investigate mechanisms of invasion success in the marine realm using ascidians as model systems.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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