Kelp forest biomass, species composition and depth distribution survey at Hansneset in Kongsfjorden, Svalbard
Bibliographic record
Abstract
Macroalgal surveys were performed at Hansneset, Blomstrand in Kongsfjorden, Svalbard, from the infralittoral fringe down to 15 m depth in June – August 2021. This dataset is part of a time series currently spanning over 25 years and complements the studies conducted in 1996/98 (Hop et al., 2012) and 2012-14 (Bartsch et al., 2016). The aim was to document changes in Arctic kelp forest dynamics in an Arctic fjord system influenced by glacial melt by repeatedly sampling the same site in a standardized manner. As ocean temperatures in the Arctic have risen substantially and underwater light climate continuously deteriorated over this time period, alterations were observed in the seaweed community, especially kelps as major coastal foundation species. This Zenodo upload contains all datasets supporting the findings of the manuscript Düsedau et al. (2023) "Arctic kelp forest decline - a consequence of melting glaciers?".
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".