Transport and thermohaline variability in <scp>B</scp>arrow <scp>C</scp>anyon on the <scp>N</scp>ortheastern <scp>C</scp>hukchi <scp>S</scp>ea <scp>S</scp>helf
Bibliographic record
Abstract
Abstract We used a 5 year time series of transport, temperature, and salinity from moorings at the head of Barrow Canyon to describe seasonal variations and construct a 37 year transport hindcast. The latter was developed from summer/winter regressions of transport against Bering‐Chukchi winds. Seasonally, the regressions differ due to baroclinicity, stratification, spatial, and seasonal variations in winds and/or the surface drag coefficients. The climatological annual cycle consists of summer downcanyon (positive and toward the Arctic Ocean) transport of ∼0.45 Sv of warm, freshwaters; fall (October–December) upcanyon transport of ∼−0.1 Sv of cooler, saltier waters; and negligible net winter (January–April) mass transport when shelf waters are saline and near‐freezing. Fall upcanyon transports may modulate shelf freezeup, and negligible winter transports could influence winter water properties. Transport variability is largest in fall and winter. Daily transport probability density functions are negatively skewed in all seasons and seasonal variations in kurtosis are a function of transport event durations. The latter may have consequences for shelf‐basin exchanges. The climatology implies that the Chukchi shelf circulation reorganizes annually: in summer ∼40% of the summer Bering Strait inflow leaves the shelf via Barrow Canyon, but from fall through winter all of it exits via the western Chukchi or Central Channel. We estimate a mean transport of ∼0.2 Sv; ∼50% less than estimates at the mouth of the canyon. Transport discrepancies may be due to inflows from the Beaufort shelf and the Chukchi shelfbreak, with the latter entering the western side of the canyon.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".