Reconstructing the various facets of dissolved organic carbon bioavailability in freshwater ecosystems
Bibliographic record
Abstract
We explored various aspects of freshwater dissolved organic carbon (DOC) lability by comparing short‐term (< 2 d) bacterial C consumption (STCC; derived from bacterial respiration measurements) with long‐term (28 d) C consumption (LTCC) in DOC bioassays in lakes, rivers, and marshes located within the same complex drainage basin in southern Québec. We also combined STCC and LTCC measurements to estimate the proportion of DOC removed, and to derive a first‐order decay constant ( k ). STCC rates were, on average, 25% higher than LTCC, and both parameters showed distinct patterns, reaching their lowest and highest values in lakes and marshes, respectively. STCC and LTCC were correlated to DOC concentration across these freshwater ecosystems, whereas in lakes, STCC was positively correlated to chlorophyll and LTCC to terrestrial C inputs. k showed no ecosystem‐specific patterns but was negatively correlated to chlorophyll across systems. The size of the DOC pools supporting STCC and LTCC, as well as k , were related to distinct components of the DOC pool, as revealed by a parallel factor analysis of fluorescent dissolved organic matter excitation‐emission spectra. Short‐ and long‐term lability and C consumption, and the resulting k , are shown to be complementary facets of DOC bioavailability, which may play very different roles on aquatic ecosystem functioning.
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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 | 0.001 | 0.001 |
| 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".