A Marine Biodiversity Observation Network for Genetic Monitoring of Hard-Bottom Communities (ARMS-MBON)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marine hard-bottom communities are undergoing severe change under the influence of multiple drivers, notably climate change, extraction of natural resources, pollution and eutrophication, habitat degradation, and invasive species. Monitoring marine biodiversity in such habitats is, however, challenging as it typically involves expensive, non-standardized, and often destructive sampling methods that limit its scalability. Differences in monitoring approaches furthermore hinders inter-comparison among monitoring programs. Here, we announce a Marine Biodiversity Observation Network (MBON) consisting of Autonomous Reef Monitoring Structures (ARMS) with the aim to assess the status and changes in benthic fauna with genomic-based methods, notably DNA metabarcoding, in combination with image-based identifications. This article presents the results of a 30-month pilot phase in which we established an operational and geographically expansive ARMS-MBON. The network currently consists of 20 observatories distributed across European coastal waters and the polar regions, in which 134 ARMS have been deployed to date. Sampling takes place annually, either as short-term deployments during the summer or as long-term deployments starting in spring. The pilot phase was used to establish a common set of standards for field sampling, genetic analysis, data management, and legal compliance, which are presented here. We also tested the potential of ARMS for combining genetic and image-based identification methods in comparative studies of benthic diversity, as well as for detecting non-indigenous species. Results show that ARMS are suitable for monitoring hard-bottom environments as they provide genetic data that can be continuously enriched, re-analyzed, and integrated with conventional data to document benthic community composition and detect non-indigenous species. Finally, we provide guidelines to expand the network and present a sustainability plan as part of the European Marine Biological Resource Centre ( www.embrc.eu ).
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it