Rendered British Columbia Continental Margin (BCCM) model output for the pacea R package
Bibliographic record
Abstract
The British Columbia Continental Margin (BCCM) model is a physical biogeochemical oceanographic model, implemented using the Regional Ocean Modeling System (ROMS). It has a horizontal resolution of 3km x 3km and a vertical discretization, based on bathymetry, of 42 depth levels that increase in resolution near the surface. The outputs are rendered here to a 2km x 2km grid, for use in the pacea R package. pacea is "An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management" freely available at https://github.com/pbs-assess/pacea . The package and the outputs saved in the formats here make it easy to view and analyse the outputs in R. See the rendered vignettes from the pacea page. Original BCCM results are from Angelica Peña at the Institute of Ocean Sciences (Fisheries and Oceans Canada). Rendering into R was done by Andrew Edwards (Pacific Biological Station, Fisheries and Oceans Canada), adapting code by Travis Tai. For model details see: Peña, M.A., Fine, I. and Callendar, W. 2019. Interannual variability in primary production and shelf-offshore transport of nutrients along the northeast Pacific Ocean margin. Deep-Sea Research II, doi:10.1016/j.dsr2.2019.104637.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.189 | 0.106 |
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".