Impact of Electromagnetic Fields from Submarine Cables on Marine Life
Bibliographic record
Abstract
Submarine cables cause ecological impacts on benthic habitats and disturb fragile ecosystems, which threaten marine biodiversity.Actions to mitigate climate change have led to the adoption of renewable energy sources such as wind, wave, and tidal energy.However, the installation of submarine cables required to harness these resources has had negative impacts on marine flora and fauna.In this context, this research aimed to determine the effects of submarine cables on marine flora and fauna through a systematic review.The PRISMA 2020 statement was used.Inclusion and exclusion criteria were based on aspects that covered the largest number of relevant studies, and studies were retrieved from five databases.The compound annual growth rate of scientific output was calculated using a digital tool (Calcuvio), and data analysis was performed using Microsoft Office Excel.The country with the highest scientific output was Poland, and the year with the highest output was 2023.The compound annual growth rate of scientific output (2005-2023) was 17.42%.The most studied type of submarine cable, based on its function, was the energy transmission cable.The most studied marine organisms were fish, and the effects of electromagnetic fields included attraction to the submarine cable, behavioral changes, reduced mobility, and altered distribution.It is recommended that research be conducted on the entire life cycle of marine species (e.g., octopuses, lobsters, jellyfish, anemones, starfish, sea urchins), as well as on submarine cables used for telecommunications transmission and their effects on marine life.
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 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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".