Collaborative approaches to multi-disciplinary monitoring of the Chukchi shelf marine ecosystem: Networks of networks for maintaining long-term Arctic observations
Bibliographic record
Abstract
In 2015, the Hanna Shoal region of the Chukchi Sea shallower than the 40 m isobath was withdrawn from outer continental shelf oil and gas exploration, a move the White House noted was designed to protect areas of “critical importance … for marine mammals, other wildlife, and wildlife habitat”. Arctic regions are projected to strongly manifest impacts of an altered climate and subsurface moored continuous observations are essential for understanding time-dependent marine processes that are likely to change in unanticipated ways. However, autonomous observations in this cold, ice-covered, and corrosive environment are difficult operations. Since 2014, the moored Chukchi Ecosystem Observatory (CEO) has been located in 45 m of water on the southern flank of Hanna Shoal just a few kilometers south of the marine protected area boundary. Concurrent measurements include physical, nutrient and carbonate chemistry, particulate, phytoplankton, zooplankton, fisheries, and marine mammal data sets. These measurements provide a unique multi-disciplinary view into the mechanistic workings of the Chukchi shelf ecosystem. Ship-based programs including the Arctic Marine Biodiversity Observation Network (AMBON) and the Distributed Biological Observatory (DBO) place the CEO within a broader spatial context of observations and provide vessel support for mooring recoveries and deployments, as well as visual marine mammal and marine bird surveys and both water column and benthic biological measurements. In turn, the CEO helps anchor the AMBON and DBO data in time with long duration and high temporal resolution sampling. The technology and the collaborative approach associated with AMBON, the CEO, and the DBO are leading to sustained observations that are expanding our ability to understand seasonality of physical, biochemical, and biological processes on Arctic shelves. These sustained observations are examining processes that contribute to short (seconds) and long (years) time scale variations, and the reasons that Hanna Shoal is of particular importance to walrus and other wildlife. Together, these programs' observations reveal consequences of wind and wave activity on ocean currents and water column hydrography, the annual cycle of nutrient draw-down and replenishment, the timing and composition of particulate matter settling, the status of lower trophic level biological communities, marine mammal activity, the distribution of ice keels, and the timing and location of fish and macrozooplankton in the water column. Such measurements are helping guide a better understanding of the ecological functioning of the biologically-rich Hanna Shoal region.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".