What evidence exists on the impact of climate change on some of the worst invasive fish and shellfish? A systematic map protocol
Bibliographic record
Abstract
Abstract Background The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has estimated that invasive alien species (IAS) might cause billions of dollars of losses every year across the world. One example is South-East Asia, where IAS have caused an estimated loss of 33.5 billion USD, affecting the environment, human health, and agricultural production. Factors associated with climate change, such as increased carbon dioxide (CO2), heavy precipitation, and elevated temperatures is expected to facilitate biological invasion, leading only to further financial and public health loss. Thus, further study is needed to identify, collate and categorise what evidence exists on the impacts of climate change on fish and shellfish species that contribute to the list of “One Hundred of the World’s Worst Invasive Alien Species” as identified by the International Union for Conservation of Nature’s (IUCN). Such mapping will identify regions more at risk of biological invasion as climate change progresses. Methods We outline a systematic mapping review protocol that follows the Guideline and Standards for Evidence Synthesis in Environmental Management and RepOrting standards for Systematic Evidence Syntheses (ROSES). We describe how peer-reviewed articles will be collected from Web of Science and Scopus, and then analyzed to create knowledge maps on the impact climate change has on invasive species. Finally, we speculate on how our results will aid future management of invasive species in the light of climate change.
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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 | 0.096 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.046 | 0.027 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".