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Record W2781642923

Oceanographic mooring designs for challenging environments

2017· article· en· W2781642923 on OpenAlexaffabout
Ben Garrett, Keath Borg, Todd Mudge, Jeremy Lawrence, James G. Bartlett

Bibliographic record

VenueOCEANS 2017 – Anchorage · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsASL Environmental Sciences (Canada)
Fundersnot available
KeywordsMooringInletOceanographyMarine engineeringArcticBathymetryDrifterEnvironmental scienceGeologyMeteorologyGeographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Arctic and subarctic conditions represent some of the most challenging environments to make long-term in situ oceanographic measurements. These areas bring the normal challenges of deep water, turbidity, uncertain bottom types and, in areas such as Cook Inlet, some of the world's largest tidal height changes and tidal currents. This paired with the presence of sea ice, which can be up to tens of meters thick and can be present for most of the year, just ratchets up the difficulties. Add in weather, ice and the lack of vessels, which often limits field time to a short window in the summer, requires equipment to work without servicing for one or more years, just spicing it up some more. This has been the crucible within which numerous mooring designs have been developed, tweaked and eventually deployed in less challenging southern latitudes. ASL has been designing and deploying mooring arrays for the waters around Alaska and around the Arctic since 1977. These have included a recent three-year mooring program of up to 11 bottom and taut-line moorings for currents, ice and water quality measurements in the fast flowing and turbid, northern reaches of Cook Inlet. One acoustic release was temporarily lost during the first deployment, but was found with the much-appreciated assistance of a local beachcomber. With modifications of the mooring designs and some analysis of NOAA bathymetry data to allow for the careful selection of mooring locations, all subsequent deployments had 100% instrument recovery rates and all without further beachcomber assistance. The Chukchi and Beaufort Seas proved less challenging for currents, but with ice keels of over 30 m draft and the odd ice island, special care had to be taken to ensure a 100% mooring recovery rate for the up to 23 moorings deployed starting in 2005 and ending in 2016. While most of the Chukchi and Beaufort deployments were at relatively safe depths of around 40 m, we tried our luck for a year with a couple of moorings at a depth of 25 m, within sight of land. In 2008, ASL designed its deepest deployed moorings at the time. With deployments in water depths up to 1000 m, the multiinstrument taut-line moorings were required to provide a relatively stable platform for an Ice Profiling Sonar only 60 m beneath the surface. After years of successful deployments in the Canadian Beaufort Sea, the mooring design was modified to accept multiple Acoustic Zooplankton and Fish Profilers for the Alaskan Beaufort Sea. These tried and tested, long-term mooring designs have made their way to more southern climates. With ultra-deep deployments of 6 to 12 months at 31 N, the experience of designing and improving deep water Arctic moorings proved invaluable, particularly in keeping wave profilers within 70 m of the surface on taut-line moorings of over 1500 m. A short taut-line mooring design that survived multiple deployments in Cook Inlet has now made it to 49 N where it is monitoring for turbidity flows near Vancouver — at least, we are hoping it still is.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.363
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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