Data for: Arctic mid-winter phytoplankton growth revealed by autonomous profilers
Bibliographic record
Abstract
These two files capture the bulk of the data behind our study "Arctic mid-winter phytoplankton growth revealed by autonomous profilers", published in Science Advances: https://advances.sciencemag.org/content/6/39/eabc2678 Specifically, "Baffin-Bay-2017-19-BGC-floats.csv" contains vertical profiles of optical, biogeochemical, and hydrographic data as sampled by the biogeochemical Argo floats. "Baffin-Bay-2017-19-lightfield-model.csv" contains vertical profiles of daily photosynthetically available radiation as modelled following A. Morel, Light and marine photosynthesis: A spectral model with geochemical and climatological implications. Prog. Oceanogr. 26, 263–306 (1991). These data were sampled by autonomous BGC Argo floats from July 2017 through July 2019 in Baffin Bay, an Arctic sea situated between Nunavut (Canada) and Greenland. Our study makes use of several other ancillary data sets. These are archived together with the complete workflow to produce the analysis and manuscript at doi:10.5281/zenodo.3945046. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it. (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System. See https://doi.org/10.17882/42182
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.147 | 0.122 |
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".