Zooplankton generation following inundation of floodplain soils: effects of vegetation type and riverine connectivity
Bibliographic record
Abstract
We investigated the potential for zooplankton to emerge following inundation of dry soils on the lower Waikato River floodplain, North Island, New Zealand. Soil cores were collected from native forest remnants, scrub (predominantly Salix spp.) and pasture, and from sites inside or outside of stopbanks, to examine the effects of vegetation type and hydrological disconnection. We hypothesised that more larger-bodied zooplankton would emerge from forested floodplain areas, and that areas with high connectivity with the river would produce more zooplankton. Zooplankton appeared from soil cores within 3 days of wetting and no new taxa arose after 12 days. Community composition differed between vegetation types, with larger bodied cladocerans and copepods dominating forested and scrub sites, and rotifers dominating pastoral sites. Connectivity did not play a statistically significant role in determining community composition. Soil conditions were implicated as important in affecting emergent zooplankton community composition, with copepods and cladocerans characteristic of sites with wetter soils and bdelloid rotifers abundant in open sites with higher soil temperatures. Our findings indicate scrub and forested floodplains can be important areas for large-bodied zooplankton production, and that maintaining vegetative heterogeneity on floodplains may promote trophic subsidies for migrating juvenile fish as floodwaters subside.
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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".