[Secondary productivity of macrobenthos in intertidal flat of Luoyuan Bay, Fujian Province of East China].
Bibliographic record
Abstract
Based on the survey data of macrobenthos in the intertidal flat of Luoyuan Bay in 2009, and by using Brey' s empirical formula, this paper estimated the macrobenthos secondary productivity, production/biomass (P/B) ratio, and their spatiotemporal distribution patterns in the flat. The annual secondary productivity and P/B ratio of the macrobenthos in the flat were estimated as 18.58 g AFDM (ash free dry mass) x m(-2) x a(-1) and 0.97, respectively, and the secondary productivity in the Spartina alterniflora marshes of the upper intertidal flat (8.97g AFDM x m(-2) a(-1)) was much lower than that in the bare flat of the lower intertidal zone (28.19 g AFDM x m(-2) x a(-1)). Since the introduction of S. alterniflora by the end of the 1980s, the secondary productivity of the macrobenthos in the flat had increased about 4 folds. Musculus senhousei, which inhabited in the lower bare flat, contributed 66.4% of the total macrobenthos secondary productivity in the entire intertidal flat. If the contribution from M. senhousei was not taken into account, the macrobenthos secondary productivity in the salt marshes would be significantly higher than that in the bare flat, and the macrobenthos secondary productivity in the intertidal flat would have less change. The successful colonization of M. senhousei was partly attributed to the changes of sediment composition enhanced by S. alterniflora due to its capability in trapping fine-grained materials; this species' feeding activities result in a remarkable increase of sediment chlorophyll a content. There was a significant positive correlation between the secondary productivity of macrobenthos and the sediment chlorophyll a and/or organic matter contents. These findings suggested that salt marshes had effects on the coastal wetland ecosystem via a number of physical and biological mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".