The effects of nutrients, fathead minnows, and submersed macrophytes on the invertebrate community and habitat quality of Delta Marsh
Bibliographic record
Abstract
The effect of nutrient addition, macrophyte removal and fathead minnow addition on the invertebrate community and habitat quality of Delta Marsh was assessed using 'in situ' enclosures in the Blind Channel. Factors important in determining the stable state of the marsh were chosen as treatments (nutrient addition, submersed macrophyte removal, and fathead minnow addition). The clear water stable state, characterized by low turbidity, low phytoplankton biomass and abundant submersed macrophytes, is most likely when nutrient loading is low, macrophytes are abundant, and top-down control from planktivorous fish is low. The turbid water state, characterized by high turbidity, high phyloplankton biomass and few submersed macrophytes, is most likely when nutrient loading is high, submersed macrophyte biomass is sparse, and top-down control is high. Inorganic nutrient addition (N and P) was found to cause phytoplankton blooms, and thus turbid conditions when submersed macrophyle biomass was relatively low. However, nutrient addition did not cause phytoplankton blooms or turbid conditions when submersed macrophytes were abundant. Addition of fathead minnows resulted in decreased densities of microinvertebrates, and thus a greater biomass of phytoplankton, due to decreased grazing pressure via the trophic cascade. Submersed macrophytes did not provide a refuge for zooplankton from predation by planktivorous young of the year fathead minnows.
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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) | 0.001 | 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".