Microbial utilization of dissolved organic carbon leached from riparian litterfall
Bibliographic record
Abstract
Dissolved organic carbon (DOC) in aquatic systems is abundant and used within stream food webs, but DOC quality is rarely studied. DOC in the leachates from the litter of five tree species (red alder, Alnus rubra; vine maple, Acer circinatum; western red cedar, Thuja plicata; western hemlock, Tsuga hetrophylla; and Douglas-fir, Pseudotsuga menziesii) were assessed for their chemistry and relative ability to support growth of heterotrophic, stream bacteria. Bacterial growth was measured using [3H]leucine incorporated into protein over 24 h of exposure to nutrient-amended leachates. Bacterial growth was greatest in deciduous and western red cedar leachates, controlling for DOC concentration. Bacterial growth rates on most leachates were greatest after 1 h and then declined in a negative exponential pattern. The DOC less than 10 kDa supported lower bacterial growth rates than DOC from whole leachates on a per milligram DOC basis. The DOC C:N atomic ratio was the best predictor of bacterial growth (r2 = 0.84). DOC release from western hemlock needles increased linearly during 7 days of leaching, whereas most red alder and western red cedar DOC was released after 1 and 2 days, respectively. Successional changes in composition of riparian forest trees may influence the stream microbial productivity based on the changes in dissolved organic carbon.
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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 | 0.000 |
| 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 | 0.001 | 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".