The FORCIS database: A global census of planktonic Foraminifera from ocean waters
Bibliographic record
Abstract
Planktonic Foraminifera are unique paleo-environmental indicators through their excellent fossil record in ocean sediments. Their distribution and diversity are affected by different environmental factors including anthropogenically forced ocean and climate change. Until now, historical changes in their distribution have not been fully assessed at the global scale. Here we present the FORCIS (Foraminifera Response to Climatic Stress) database on foraminiferal species diversity and distribution in the global ocean from 1910 until 2018 including published and unpublished data. The FORCIS database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump, and contains ~22,000, ~157,000, ~9,000, ~400 subsamples, respectively (one single plankton aliquot collected within a depth range, time interval, size fraction range, at a single location) from each category. Our database provides a perspective of the distribution patterns of planktonic Foraminifera in the global ocean on large spatial (regional to basin scale, and at the vertical scale), and temporal (seasonal to interdecadal) scales over the past century.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".