Contribution of photic and aphotic <scp>N<sub>2</sub></scp> fixation to production in an oligotrophic sea
Bibliographic record
Abstract
Abstract Dinitrogen (N2) fixation was investigated at a pelagic station in the oligotrophic waters of the northern Gulf of Aqaba, Red Sea, between February 2016 and December 2018. In situ 15N2 and 13C incubations were used to evaluate photic and aphotic N2 fixation rates and diazotrophic contribution to water column productivity. N2 fixation rates were typically low (below detection to <0.5 nmol N L−1 d−1). Maximal rates of 3.1 nmol L−1 d−1, measured at 100 m when conspicuous slicks of the cyanobacterium Trichodesmium appeared on the surface water. Amplicon sequencing of nifH demonstrated that non‐cyanobacterial diazotrophs, mostly α‐ and γ‐proteobacteria comprised the majority (82–100%) of amplicon sequence variants retrieved from photic and aphotic depths when low N2 fixation rates were measured, while amplicons representing cyanobacteria were nearly absent, but appeared in low abundance (~ 2%) when maximal rates were measured. During the stratified summer‐period, water‐column N2 fixation rates (10–75 μmol m−2 d−1) comprised ~ 1–40% of new production (NP). During the winter mixing period N2 fixation rates were considerably higher (11–242 μmol m−2 d−1) but made up only <1% of NP. This is because, during this period, nitrate supplied to the photic zone by the vertical mixing becomes the major N source for NP. We conclude that on an annual average, diazotrophy plays a minor role in the NP of the Gulf. The major “new” nitrogen sources are cross‐thermocline turbulent diffusion of nitrate during summer stratification and vertical mixing during the fall–winter.
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 | 0.000 |
| 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) | 0.000 | 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".