Observed and modelled data from Discovery Islands (British Columbia, Canada) for summer 2019
Bibliographic record
Abstract
This repository contains two datasets associated with the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al. In that work, an application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. The two datasets are described below: 1) Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations. "obs_model_pairs.zip" contains 114 netcdf files. Each file has observed and modelled profiles for temperature and salinity at one station within the Discovery Islands region. 2) Modelled mean summer circulation and conditions in Bute Inlet (a long, deep fjord within the Discovery Islands model domain) for the two model configurations in the mentioned publication. The "Baseline" simulation used observed initial conditions in Bute Inlet, while the "Sensitivity" simulation removed the observed cold subsurface water mass from the initial profiles. In this dataset, we provide 29-day averages of the following variables in a transect along Bute Inlet (the averaging properly removes tidal effects): potential temperature, density, along-inlet velocity, and Brunt-Väisälä frequency (N^2). "TransectData_baseline.nc" has the modelled mean variables from the Baseline simulation "TransectData_sensitivity.nc" has the modelled mean variables from the Sensitivity simulation The preprint associated with the publication is found at EGUsphere - Persistence of a Subsurface Water Mass in a Deep Mid-Latitude Fjord (copernicus.org). Please refer to the accepted version of the mansucript, rather than the preprint (link not available at the time of the creation of this repository).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".