Drivers of ecosystem metabolism in restored and natural prairie wetlands
Bibliographic record
Abstract
Elucidating drivers of aquatic ecosystem metabolism is key to forecasting how inland waters will respond to anthropogenic changes. We quantified gross primary production (GPP), respiration (ER), and net ecosystem production (NEP) in a natural and two restored prairie wetlands (one “older” and one “recently” restored) and identified drivers of temporal variation. GPP and ER were highest in the older restored wetland, followed by the natural and recently restored sites. The natural wetland was the only net autotrophic site. Metabolic differences could not be definitively tied to restoration history, but were consistent with previous studies of restored wetlands. Wetlands showed similar metabolic responses to abiotic variables (photosynthetically active radiation, wind speed, temperature), but differed in the direct and interactive influences of biotic factors (submersed aquatic vegetation, phytoplankton). Drivers and patterns of metabolism suggested the importance of light over nutrient limitation and the dominance of autochthonous production. Such similarity in ecosystem metabolism between prairie wetlands and shallow lakes highlights the need for a unifying metabolic theory for small and productive aquatic ecosystems.
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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 | 0.000 | 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".