PANABIO: a point-referenced PAN-Arctic data collection of benthic BIOtas
Bibliographic record
Abstract
Abstract. Profound environmental changes, such as drastic sea-ice decline, leave large-scale ecological footprints on the distribution and composition of marine biota in the Arctic. Currently, the impact of such stressors is not sufficiently understood due to the lack of pan-Arctic data that allow for estimating ecological baselines as well as modelling current and forecast potential changes in benthic biodiversity and ecosystem functioning. Here, we introduce the PAN-Arctic data collection of benthic BIOtas (PANABIO) and discuss its timeliness, potential, and details of its further development. The data collection contains individual datasets with records (presence, counts, abundance, or biomass) of benthic fauna, usually at genus level or species level, which were identified in field samples obtained at point-referenced locations (stations) by means of grabs, towed gear, or seabed imaging. The data cover the entire pan-Arctic realm, i.e. the central Arctic Ocean, Chukchi Sea, East Siberian Sea, Laptev Sea, Kara Sea, Barents Sea (including the White Sea), Svalbard waters, Greenland Sea, Norwegian Sea, Canadian Archipelago, Beaufort Sea, and Bering Sea as well as some adjacent sub-Arctic regions (Sea of Japan, Gulf of Okhotsk). Currently (as of 14 December 2023), PANABIO includes 27 datasets with a total of 126 388 records of 2978 taxa collected from 11 555 samples taken at 10 596 stations during 1095 cruises between 1800 and 2014. These numbers will increase with more data becoming available over time through contributions from PANABIO users. The data collection is available in a PostgreSQL-based data warehouse that can be accessed and queried through an open-access front-end web service at https://critterbase.awi.de/panabio (last access: 27 February 2024). A snapshot of the current data collection and its 27 individual datasets is also available from the data publisher PANGAEA (https://doi.org/10.1594/PANGAEA.963640, Piepenburg et al., 2023).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".