Nutrient supply and biological response to wind‐induced mixing, inertial motion, internal waves, and currents in the northern <scp>C</scp>hukchi <scp>S</scp>ea
Bibliographic record
Abstract
Abstract A fixed‐point observation station was set up in the northern Chukchi Sea during autumn 2013, and for about 2 weeks conductivity‐temperature‐depth (CTD)/water samplings (6 h) and microstructure turbulence measurements (2 to 3 times a day) were performed. This enabled us to estimate vertical nutrient fluxes and the impact of different types of turbulent mixing on biological activity. There have been no such fixed‐point observations in this region, where incoming low‐salinity water from the Pacific Ocean, river water, and sea‐ice meltwater promote a strong pycnocline (halocline) that stabilizes the water column. Previous studies have suggested that because of the strong pycnocline, wind‐induced ocean mixing could not change the stratification to impact biological activity. However, the present study indicates that a combined effect of an uplifted pycnocline accompanied by wind‐induced inertial motion and turbulent mixing caused by intense gale‐force winds (>10 m s −1 ) did result in increases in upward nutrient fluxes, primary productivity, and phytoplankton biomass, particularly large phytoplankton such as diatoms. Convective mixing associated with internal waves around the pycnocline also increased the upward nutrient fluxes and might have an impact on biological activity there. For diatom production at the fixed‐point observation station, it was essential that silicate was supplied from a subsurface silicate maximum, a new feature that we identified during autumn in the northern Chukchi Sea. Water mass distributions obtained from wide‐area observations suggest that the subsurface silicate maximum water was possibly derived from the ventilated halocline in the Canada Basin.
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 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 | 0.004 | 0.007 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".