Measurement of Denitrification in the Changjiang River
Bibliographic record
Abstract
Environmental Context. Rivers are carrying an increased load of nitrogen-based matter (nitrates, nitrites) resulting from, among others, increased use of agricultural fertilizers. This nitrogen enrichment leads to a proliferation of plant life in the receiving water body, which in turn reduces the dissolved oxygen content and can cause the extinction of other organisms. Rivers can reduce their nitrogen levels through denitrification, the bacterially mediated transformation of dissolved nitrates and nitrites to gaseous N2 and N2O. This paper reports the first examination of denitrification in China’s largest river, the Changjiang (Yangtze) River, to understand the details of riverine denitrification and its role on controlling nitrogen export. Abstract. Rivers are an important link between terrestrial and aquatic ecosystems for nitrogen cycling, while denitrification plays a key role in riverine nitrogen removal. Denitrification was first examined in China’s largest river, the Changjiang River, by using a whole-reach method. The production rates of N2 by means of denitrification were 2.82 ± 1.18 and 5.74 ± 2.92 mmol(N) m-2 h-1 in October 2002 and March 2003, respectively, and the rates of N2O production were 1.98 ± 1.48 and 581 ± 1937 nmol(N) m-2 hr-1 in August and October 2002, respectively. Nitrogen removal through N2 and N2O emission accounted for 1–2% of NO3–-N flux through October 2002 to March 2003. Continued measurement throughout a whole year period after the construction of the Three Gorges Reservoir will provide more understanding of riverine denitrification and its role on controlling nitrogen export.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".