Predicting the potential distribution of the vase tunicate Ciona intestinalis in Canadian waters: informing a risk assessment
Bibliographic record
Abstract
Abstract Therriault, T. W., and Herborg, L-M. 2008. Predicting the potential distribution of the vase tunicate Ciona intestinalis in Canadian waters: informing a risk assessment. – ICES Journal of Marine Science, 65: 788–794. A crucial step in characterizing the potential risk posed by non-native species is determining whether a potential invader can establish in the introduced range and what its potential distribution could be. To this end, various environmental models ranging from simple to complex have been applied to predict the potential distribution of an invader, with varying levels of success. Recently, in marine waters, tunicates have received much attention, largely because of their negative impacts on shellfish aquaculture. One of these species is the vase tunicate Ciona intestinalis, which recently has had a negative impact on aquaculture operations in Atlantic Canada and could pose a risk in Pacific Canada. To inform the risk assessment of this species, we evaluated two different types of environmental model. Simple models based on reported temperature or salinity tolerances were relatively uninformative, because almost all waters were deemed suitable. In contrast, a more complex genetic algorithm for rule-set prediction (GARP) environmental niche model, based on documented Canadian occurrence points, provided informative projections of the potential distribution in Canadian waters. In addition to informing risk assessments, these predictions can be used to focus monitoring activities, particularly towards vectors that could transport C. intestinalis to these favourable environments.
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.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".