Integrating global chlorophyll data from 1890 to 2010
Bibliographic record
Abstract
Understanding large-scale phytoplankton dynamics requires accurate, multi-decadal measurements of abundance and distribution. Since 1890, marine phytoplankton abundance has been assessed using a diverse range of sensors and observational platforms, and inter-calibrating these data have been challenging. Consequently, syntheses of historical phytoplankton data have been rarely attempted, and the need for accurate, long-term assessments of phytoplankton abundance and distribution is commonly acknowledged. Here, we derive quantitative indices of phytoplankton abundance from measurements of upper ocean transparency and color-calibrated with direct measurements of surface chlorophyll. The strong correlation and linear scaling of the predicted data enabled the construction of a comprehensive, globally intercalibrated chlorophyll time series from 1890 to 2010. The calibrated chlorophyll data reproduced the well-established spatial features of phytoplankton surface biomass and were strongly correlated with chlorophyll concentration derived from two independent remote sensing platforms discontinuously available since 1978. These results suggest that with careful statistical treatment it is possible to generate a globally integrated chlorophyll time series extending 120 years into the past. This database, which is available in the web appendices of this paper, may enable new insights in the areas of climate science, biogeochemical cycling, and marine ecosystem structure and functioning over the past century.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".