Toxicity of crude oil: a systematic review of the acute impacts on Cnidaria
Bibliographic record
Abstract
Cnidarians are ecosystem engineers that play a fundamental role in food webs. However, they are increasingly threatened by anthropogenic activities, such as oil spills, which can cause acute sublethal effects and in some cases, mortality. The literature on this theme is limited and fragmented, with diverse objectives and methodologies, making it challenging to identify consistent patterns and knowledge gaps. This article aims to provide a systematic review of the acute impacts of crude oil and petroleum hydrocarbons on members of the Phylum Cnidaria, to synthesize and integrate the available data. The research followed the PRISMA 2020 guidelines, and employed the search terms: “ crude oil” AND “acute” AND (Cnidaria OR coral), along with the names of each Cnidarian class. The search was restricted to articles published in English from 2013 to 2023, excluding studies that did not directly address the core topic. The number of records collected was relatively low, with only 30 articles identified from 20 studies. The highest number of publications occurred in 2022, and the USA was the most represented country. The Class Anthozoa, particularly corals (order Scleractinia), was the most studied. Research focused on the effects of crude oil on gametes, embryos, larvae, and adults, with most studies examining adult organisms. The majority (56.6%) utilized mixtures of crude oil hydrocarbons, while a smaller proportion (36.6%) focused on pure aromatic hydrocarbons. Most experiments employed water accommodated fractions (36.6%) and continuous flow system (20%). The most common exposure durations (30%) were 96 and 48 h, with exposure times ranging from 18 h to several days. There was significant variation in methodological approaches, underscoring the need for standardized protocols. Research on adult cnidarians primarily assessed health and morphology, lethality, genetic alterations, and symbiont efficiency. In contrast, studies on early life stages focused on lethality, larval metamorphosis, and settlement. Overall, the results indicate that responses vary considerably, particularly concerning exposure time and oil concentration, with sublethal effects at different levels. Further studies incorporating a broader range of species, novel approaches, and the combined effects of elevated temperature and UV radiation are crucial to advancing our understanding of oil impacts on cnidarians.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".