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Record W4393589239 · doi:10.5281/zenodo.8079891

High-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category: Dataset and Code

2023· dataset· en· W4393589239 on OpenAlexaboutno aff
Lukas L. Taenzer, Glen Gawarkiewicz, Albert J. Plueddemann

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsContinental shelfStratification (seeds)OceanographyOff the shelfEvent (particle physics)MeteorologyGeologyClimatologyEnvironmental scienceGeographyComputer scienceBiology

Abstract

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Dataset of identified high-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category, as well as the associated code to reproduce the figures of accompanying publication. The data have been recorded by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Array. Accompanying publication: Taenzer, L.L., Gawarkiewicz, G., and Plueddemann, A. (2023). Categorization of High-Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf. Journal of Geophysical Research: Oceans, 128, e2022JC019625. https://doi.org/10.1029/2022JC019625 Contact: Lukas Taenzer (lukas.taenzer@whoi.edu) Structure of provided code: PART A: Local high-wind ocean impact analysis PART B: Analysis of seasonal high-wind impacts on stratification PART C: High-wind event categorization and the impact of different categories Code has been written in MATLAB R2023a. Output: Processed data of all locally detected high-wind events incl. scalar forcing and shelf impact estimates as well as their corresponding high-wind event category: 'OOIcp_HighWindEvents_ScalarMetrics.nc' (see userflag 'save_peak_ooi') See README_HighWindEvents_ScalarMetrics for further details and license. Figures 2, 3, 4, 5, 6, 7, 8, and 9 of accompanying publication saved as .png file (always) saves as .eps file (see userflag 'save_fig_eps') Input for Analysis: Gridded Hydrography and Bulk Air-Sea interactions time series observed by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) (Taenzer et al., 2023). The required fields to reproduce the results of the accompanying publication are provided: Input/OOIcp_Met_Combined.nc Input/OOIcp_CTD_ISSM_stat.nc Input/OOIcp_CTD_PMUI_prof.nc High-wind event categorization based on their spatio-temporal sea level pressure and temporal surface wind stress signatures around/at the OOI Coastal Pioneer Array location: Input/storm_type_2015-2021_v5.mat Additional input for reproducing figures: Manually determined cyclone tracks for cyclones that occur during the fall destratification seasons 2015-2021: Input/stormtracks_cyclones_20152021_save.mat ERA5 sea level pressure data (Hersbach et al., 2018) on a 6-hour temporal and a 1°x1° spatial resolution for the time period 2015-01-01 to 2022-06-30 and across the Eastern US, Canada, and the Northwest Atlantic with the OOI Coastal Pioneer Array in the center Input/ERA5_6h_2015-2022_region_1x1.mat

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.069
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.026

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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