Relative independence of organic carbon transport and processing in a large temperate river: The Hudson River as both pipe and reactor
Bibliographic record
Abstract
Bacterial respiration (BR) of organic matter is an important flux in the carbon budgets of large rivers, yet the regulation of BR and the relationship of this respiration to various organic matter sources is poorly understood. Using detailed spatial transects, we evaluated transport and consumption of dissolved organic matter in the Hudson River estuary, and compared both with BR. Dissolved organic carbon (DOC) concentration, long‐term DOC lability, and in situ BR were measured at 24 stations on each of five transects. DOC lability, measured in long‐term bioassays, averaged 15 µg L−1 d−1 and was similar to the average net rate of decline of DOC from the upper to lower estuary. BR averaged 156 µg L−1 d−1 C, far exceeding the net downriver DOC decline and measured DOC lability. BR was well predicted by a model that included seston, chlorophyll, and DOC consumption. Rate coefficients derived from this model indicate that BR is primarily supported by carbon derived from seston and chlorophyll. Changes in DOC concentration along the Hudson flow path were well predicted from a combination of freshwater input, DOC concentration in the headwaters, and long‐term DOC lability. Although most of the total respiration is due to free‐living bacteria and thus mediated by DOC, <20% of this respiration is actually supported by DOC loaded in the headwaters, and transported downstream. The Hudson River, therefore, acts as a pipe transporting dissolved terrestrial organic matter seaward while also functioning as a reactor where intense bacterial activity degrades organic matter associated primarily with particles or generated locally.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".