Code to produce figures in "Satellite-derived global-ocean phytoplankton phenology indices"
Bibliographic record
Abstract
This is the first public release of python code used to produce figures for "Satellite-derived global-ocean phytoplankton phenology indices" (Nicholson et al. 2025). The global phytoplankton phenology dataset produced using OC-CCI chlorophyll-a data includes bloom initiation, termination, and duration for three different methods: threshold (TS), cumulative threshold (CS), and rate of change (RC). Products are provided at 25 km, 9 km, and 4 km spatial resolution. All methods and processing are described in the companion paper (Nicholson et al., 2025, https://doi.org/10.5194/essd-2024-21). Data Sources: The dataset is published in the Zenodo repository under the following DOIs, 4 km: https://doi.org/10.5281/zenodo.8402932, 9 km: https://doi.org/10.5281/zenodo.8402847 and 25 km: https://doi.org/10.5281/zenodo.8402823 (Nicholson et al., 2023a, b, c). For more information, see the README and associated documentation. License: Code: MIT License Data: CC BY 4.0
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.224 | 0.241 |
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".