Revisiting the distribution of oceanic N2 fixation and estimating diazotrophic contribution to marine production
Bibliographic record
Abstract
Abstract Marine N2 fixation supports a significant portion of oceanic primary production by making N2 bioavailable to planktonic communities, in the process influencing atmosphere-ocean carbon fluxes and our global climate. However, the geographical distribution and controlling factors of marine N2 fixation remain elusive largely due to sparse observations. Here we present unprecedented high-resolution underway N2 fixation estimates across over 6000 kilometers of the western North Atlantic. Unexpectedly, we find increasing N2 fixation rates from the oligotrophic Sargasso Sea to North America coastal waters, driven primarily by cyanobacterial diazotrophs. N2 fixation is best correlated to phosphorus availability and chlorophyll-a concentration. Globally, intense N2 fixation activity in the coastal oceans is validated by a meta-analysis of published observations and we estimate the annual coastal N2 fixation flux to be 16.7 Tg N. This study broadens the biogeography of N2 fixation, highlights the interplay of regulating factors, and reveals thriving diazotrophic communities in coastal waters with potential significance to the global nitrogen and carbon cycles.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".