Global oceanic diazotroph database version 2 and elevated estimate of global oceanic N <sub>2</sub> fixation
Bibliographic record
Abstract
Abstract. Marine diazotrophs convert dinitrogen (N2) in seawater into bioavailable nitrogen (N), contributing approximately half of the external input of bioavailable N to the global ocean. A global oceanic diazotroph database was previously published in 2012. Here, we compiled version 2 of the database by adding 23,095 in situ measurements of marine diazotrophic abundance and N2 fixation rates published in the past decade, increasing the number of N2 fixation rates and microscopic and qPCR-based diazotrophic abundance data by 140 %, 26 % and 443 %, respectively. Although the updated database expanded spatial coverage considerably, particularly in the Indian Ocean, the data distribution was still not uniform and most data were sampled in the surface Pacific and Atlantic Oceans. By summing the arithmetic means of the N2 fixation rates in each ocean basin, the updated database substantially increased the estimate of global oceanic N2 fixation from 137 ± 9 Tg N yr-1 using the old database to 260 ± 20 Tg N yr-1 (mean ± standard error). However, using geometric means instead, the updated database gave an estimate of global oceanic N2 fixation (60 Tg N yr-1) similar to that estimated from the old database (62 Tg N yr-1), while the new estimate had a larger uncertainty (confidence intervals based on one standard error: 47 – 107 Tg N yr-1 versus 52 – 73 Tg N yr-1), mostly attributable to elevated uncertainties in the Pacific Ocean. An analysis comparing N2 fixation rates measured at the same months and location (1° × 1° grids) showed that the new 15N2 dissolution method obtained N2 fixation rates higher than the conventional 15N2 bubble method in 65 % of cases, with this percentage increasing when the N2 fixation rates were high (> approximately 3 μmol N m-3 d-1 using the 15N2 dissolution method). With greatly increased data points, this version 2 of the global oceanic diazotrophic database can support future studies in marine ecology and biogeochemistry. The database is stored at the Figshare repository (https://doi.org/10.6084/m9.figshare.21677687) (Shao et al., 2022).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".