Functional processes versus state variables: interstitial organic matter pathways in floodplain habitats
Bibliographic record
Abstract
We assessed the potential rates of two microbial processes, cellulose decomposition potential (CDP) and hydrolytic activity, to explore the relationship of these functional measurements with several state variables of organic matter. Samples were collected from two interstitial sites in the main channel of the Rhône River and 10 sites on the alluvial floodplain representing five habitat types defined by sediment size (fine versus coarse sediments) and interstitial water origin (surface water versus groundwater). Although sites with fine sediments had more interstitial total organic matter, refractory and biodegradable dissolved organic carbon concentrations did not differ among habitat types. Unlike the floodplain, the main channel had high CDP and low hydrolytic activity. In the floodplain, functional measurements varied consistently, and both CDP and hydrolytic activity were lowest at the sites with coarse sediments. Our data imply that microbially mediated processes are different in the main channel and the floodplain and that low levels of organic matter in the coarse sediments are probably due to slow rates of accrual rather than rapid rates of decomposition. Furthermore, the lack of correlation between functional variables and dissolved organic carbon concentrations illustrates the dangers of interpreting ecosystem processes or ecological integrity based solely on state variables.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".