DNA metabarcoding data from Autonomous Reef Monitoring Structures (ARMS) deployed around Calvert Island British Columbia
Bibliographic record
Abstract
Autonomous Reef Monitoring Structures (ARMS) provide a standardized framework to monitor marine biodiversity. Currently, over 1,600 ARMS have been deployed globally across a number of organisations and geographical regions. Most of these deployments are related to coral reef systems, and relatively few deployments have been associated with temperate reefs or kelp forests. This data package contains links to genomic resources obtained from our use of ARMS to test whether the presence of canopy-forming kelps would influence rates of recruitment of invertebrates and seaweeds on temperate near-shore rocky reefs. Data collection was carried out at 12 locations in Queen Charlotte Sound, British Columbia. Of these sites, four were kelp beds dominated by Nereocystis luetkeana, four were kelp beds dominated by Macrocystis pyrifera, and four were considered to be urchin barrens. One ARMS unit was deployed at each site. The first deployment lasted from autumn 2016 to summer 2017 (10-11 months), the second deployment lasted from autumn 2017 to autumn 2020 (36 months). For each deployment, all methods used for assembly, collection, photography, and biological sampling were carried out following the protocol described by the Global ARMS Program (Smithsonian Institution: https://naturalhistory.si.edu/research/global-arms-program).
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".