Knowledge needs in economic costs of invasive species facilitated by canalization
Bibliographic record
Abstract
Abstract As strategic transport infrastructures, canals provide wide-ranging economic benefits as well as save energy and reduce CO2 emissions. At the same time, by connecting previously biogeographically-isolated systems, they serve as corridors for the introduction and spread of aquatic alien species, potentially leading to unforeseen ecological and economic impacts. To date, there has been no attempt to quantify the reported economic costs of these species. Here, we used the InvaCost database on the monetary impact of invasive alien species to identify the costs of species whose introduction and spread have been linked to the operation of three major canal systems: the European Inland Canals, Suez Canal and Panama Canal. While we identified a staggering number of alien species that have been spread via these systems, monetary costs have been reported only for a few of these known invasive aliens. A total of $33.6 million in costs have been reported from invasive species linked to European Inland Canals (the fishhook waterflea Cercopagis pengoi and the zebra mussel Dreissena polymorpha) and $8.6 million linked to the Suez Canal (the silver-cheeked toadfish Lagocephalus sceleratus, the lionfish Pterois miles, and the nomad jellyfish Rhopilema nomadica), but no recorded costs were yet recorded for species which invasion was facilitated by the Panama Canal. We thus identified a pervasive lack of information on the monetary costs of invasions facilitated by anthropogenically-created corridors, such as canals. In highlighting the uneven and lacking distribution of costs, we suggest those benefiting from the creation of canals are not necessarily the same as those paying the incurred costs, and urge greater recognition and reporting of impacts.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".