Seasonal sea ice cover as principal driver of spatial and temporal variation in depth extension and annual production of kelp in Greenland
Bibliographic record
Abstract
We studied the depth distribution and production of kelp along the Greenland coast spanning Arctic to sub-Arctic conditions from 78 ºN to 64 ºN. This covers a wide range of sea ice conditions and water temperatures, with those presently realized in the south likely to move northwards in a warmer future. Kelp forests occurred along the entire latitudinal range, and their depth extension and production increased southwards presumably in response to longer annual ice-free periods and higher water temperature. The depth limit of 10% kelp cover was 9-14 m at the northernmost sites (77-78 ºN) with only 94-133 ice-free days per year, but extended to depths of 21-33 m further south (73 ºN-64 ºN) where >160 days per year were ice-free, and annual production of Saccharina longicruris and S. latissima, measured as the size of the annual blade, ranged up to sevenfold among sites. The duration of the open-water period, which integrates light and temperature conditions on an annual basis, was the best predictor (relative to summer water temperature) of kelp production along the latitude gradient, explaining up to 92% of the variation in depth extension and 80% of the variation in kelp production. In a decadal time series from a high Arctic site (74 ºN), inter-annual variation in sea ice cover also explained a major part (up to 47%) of the variation in kelp production. Both spatial and temporal data sets thereby support the prediction that northern kelps will play a larger role in the coastal marine ecosystem in a warmer future as the length of the open-water period increases. As kelps increase carbon-flow and habitat diversity, an expansion of kelp forests may exert cascading effects on the coastal Arctic ecosystem.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".