Processing and Analysis of National Oceanic and Atmospheric Administration US West Coast 2022 and 2023 Fisheries Survey Data: Exploring a Robust Data Processing Modus Operandi with Various Collection Methods
Bibliographic record
Abstract
The National Oceanic and Atmospheric Administration‘s (NOAA) Northwest Fisheries Science Center’s (NWFSC) Fishery Resource Analysis and Monitoring Division (FRAM) performs multiple fisheries surveys aboard NOAA research vessels and chartered commercial fishery vessels along the US west coast stretching from San Diego, California, to the Strait of Juan de Fuca off northern Washington. In partnership with Fisheries and Oceans Canada, the Hake survey is extended north along the Canadian West Coast, to Haida Gwaii, British Columbia. Data from these surveys inform the implementation of the Magnuson-Stevens Fishery Conservation and Management Act of 2007. For the purposes of the Groundfish Survey, FRAM personnel utilize a trawl net to sample the local groundfish population at survey sites for shipboard analysis and stock assessment. In addition, Conductivity, Temperature, and Depth instruments (CTDs) are mounted to the trawl net with the purpose of acquiring oceanographic data at each survey site in conjunction with the fishery data. This form of data collection is unique to the Groundfish survey, as Hake and Hook and Line survey data are collected via traditional winch-based operations. Though other oceanographic variables are collected and analyzed, such as chlorophyll fluorescence (mg/m3) and turbidity (Normalized Turbidity Units - NTU) during survey operations, we are especially interested in dissolved oxygen (DO) data collected from the two oxygen sensors attached to the CTD payload (SBE43 Dissolved Oxygen Sensor, Aanderaa Optode 4330F Optical Dissolved Oxygen Sensor). Here, we utilize the manufacturer-recommended Sea-Bird SBE Processing pathway, and a set of scripts created within Python to process and visualize the data as a function of time, and to create near-bottom average values of all parameters. We also make maps of the concentration of DO along the seafloor over space for each year. Our results suggest that near-bottom average values of all parameters. In addition, we make maps of the concentration of DO along the seafloor over space for each year. Our results suggest that near-bottom dissolved oxygen distributions during the upwelling season are consistent with recent Pacific Northwest-wide studies inshore of the continental shelf break (~200 m). Given the widespread and increasing near-bottom hypoxia in the coastal ocean off the United States Pacific Northwest, we strongly recommend that NOAA continue to collect oceanographic data, especially dissolved oxygen data during survey operations to better understand spatial and temporal variability of hypoxic zones and their relationship to fish distributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".