The re-use of offshore platforms as ecological observatories
Bibliographic record
Abstract
The high number of offshore platforms at the end of their productive phase offers the opportunity of their re-use and the development of effective management solutions, such as the possibility of utilizing them as ecological observatories for monitoring marine ecosystems and their biological resources. Here, through a multiparametric observatory deployed at an unproductive offshore platform, located in the Central Adriatic Sea (Mediterranean Sea), we collected data for 13 months on benthopelagic fish assemblage and habitat conditions. A total of 155.5 h of high-frequency (30 min) video-monitoring, recorded higher fish abundances during spring-summer periods during daytime, while fish diversity was highest in autumn. Some environmental variables contributed significantly to explain the overall community variance. Our results suggest that offshore platforms can be re-converted into ecological observatories, to collect relevant amounts of information that can be difficulty obtained with alternative approaches, contributing to our understanding of changes occurring in open water ecosystems. • No more productive offshore platforms can act as continuous fish monitoring systems. • Abundance was higher at daytime and warmer periods, and diversity in autumn. • Ecological dynamics, mirrored by species diversity, showed a seasonal trend. • Oceanographic variables mostly drive the overall community variance. • Continuous monitoring systems are highly useful for management practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".