How Marine Megabenthos Fauna Responds to River Discharge and Artificial Flood in Large River Estuary
Bibliographic record
Abstract
ABSTRACT Estuaries are ecologically sensitive areas influenced by river regulation. Knowledge of how marine megabenthos responds to river regulation and artificial flooding events remains limited. The study aims to provide a comprehensive understanding of the impacts of river regulation on marine megabenthic fauna. The oceanographic investigations were conducted at the Yellow River Estuary and its adjacent area during three distinct periods of river discharge, that is, spring low‐flow (IA), summer low‐flow (IB), and artificial flood (IC) periods. Samples of megabenthos and 14 mainly environmental parameters were investigated during different periods. The comprehensive indices of biodiversity (C‐diversity), phylogeny (C‐phyl), and stability (C‐stability) were synthesized from 24 basic indices to characterize the overall traits of megabenthos faunas. All 210 species were collected during three periods, belonging to 16 classes and 51 orders. The typical estuarine communities were mainly distributed in the key area within 30 km of the estuary during the low‐flow periods. The estuarine community presented with low biodiversity (C‐diversity = 0.15) but high stability (C‐stability = 0.64) during the IA period, followed by an increase in biodiversity during the IB period (C‐diversity = 0.35, C‐phyl = 0.69). The community underwent dramatic changes during the IC period, which bifurcated into distinct northern and southern groups. The northern community maintained high biodiversity and homeostasis (C‐stability = 0.60), whereas the southern stability decreased sharply (0.11). On the whole, the artificial flood reshaped the geographical distribution of megabenthic fauna in the estuarine area within 20 days, mainly as a result of the dramatic changes in seawater salinity and nutrient structure caused by flow pulse. An eco‐friendly discharge management of the Yellow River is needed to mitigate the impact.
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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 | 0.000 | 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".