Three Gorges Dam: Differential determinants and spatial-temporal effects of the change of snail density
Bibliographic record
Abstract
Abstract BACKGROUND The abundance of Oncomelania hupensis snail can promote the transmission of schistosomiasis japonica. Snail distribution varies spatially and temporally in different geographical regions. Hence, we investigated differential drivers of snail density between the downstream and upstream areas of Three Gorges Dam (TGD), and spatial-temporal changes in snail abundance.METHODS We deployed the snail survey at 200 sites over 5 years to monitor a dynamic change in snail abundance within the Yangtze River basin. Corresponding variables that might affect snail abundance, such as Meteorology, vegetation, terrain, and economy, were collected from multiple data sources. We conducted the Bayesian spatial-temporal modeling framework to investigate the differential determinants and spatial-temporal effects of the change of snail density.RESULTS Obvious volatility for snail density was detected in the downstream area of TGD, whilst a small increment in the upstream area. For the downstream area of TGD, Snail density was positively associated with the average minimum temperature in January of the same year, annual normalized difference vegetation index of the previous year (NDVI), the 2nd quartile of average annual relative humidity of the previous year (RH), the 3rd quartile of RH, the 4th quartile of RH. Snail density was negatively associated with the average maximum temperature in July of the previous year, and annual night-time light of the previous year. An approximately inverted “U” curve of relative risk was detected among sites with a greater average annual ground surface temperature of the previous year. For the upstream area, snail density was positively associated with NDVI, the 2nd quartile of total precipitation of the previous year (Pre), the 3rd quartile of Pre, and the 4th quartile of Pre. Snail density was negatively associated with Slope.CONCLUSIONS Collectively, our study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation, and human activity were the main drivers affecting the snail abundance in the downstream area of TGD, while precipitation, slope, and vegetation were the main drivers affecting the upstream snail abundance. This evidence can assist the authorities to execute more precise strategies for snail investigation and control.
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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".