Horizon scanning for potentially invasive non-native marine species to inform trans-boundary conservation management – Example of the northern Gulf of Mexico
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
Prevention of non-native species introductions and establishment is essential to avoid adverse impacts of invasive species in marine environments. To identify potential new invasive species and inform non-native species management options for the northern Gulf of Mexico (Alabama, Mississippi, Louisiana, Texas), 138 marine species were risk screened for current and future climate conditions using the Aquatic Species Invasiveness Screening Kit. Species were risk-ranked as low, medium, high, and very high risk based on separate (calibrated) thresholds for fishes, tunicates, and invertebrates. In the basic screening, 15 fishes, two tunicates, and 26 invertebrates were classified as high or very high risk under current climate conditions. Whereas, under future climate conditions, 16 fishes, three tunicates, and 33 invertebrates were classified as high or very high risk. Very high risk species included: California scorpionfish Scorpaena guttata , red scorpionfish Scorpaena scrofa , purple whelk Rapana venosa , and Santo Domingo false mussel Mytilopsis sallei under both current and future climates, with weedy scorpionfish Rhinopias frondosa , Papuan scorpionfish Scorpaenopsis papuensis , daggertooth pike conger Muraenesox cinereus , yellowfin scorpionfish Scorpaenopsis neglecta , tassled scorpionfish Scorpaenopsis oxycephalus , brush-clawed shore crab Hemigrapsus takanoi , honeycomb oyster Hyotissa hyotis , carinate rock shell Indothais lacera , and Asian green mussel Perna viridis under climate change conditions only. This study provides evidence to inform trans-boundary management plans across the five Gulf of Mexico states to prevent, detect, and respond rapidly to new species arrivals.
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 | 0.001 |
| 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) | 0.002 | 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