Exploring the Effects of Landscape Metrics in Water Quality, Ave River Basin Case Study
Why this work is in the frame
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Bibliographic record
Abstract
Ave River Basin, located in the northern region of Portugal, was once tagged as one of the most polluted of Europe. Although many authors have given prominence to point source pressures, the present study reveals challenging results, by exposing strong effects of landscape metrics in water quality. In twelve sampling sites, the Portuguese benthic macroinvertebrate index (IPtIN) was measured during the winter and summer of 2017. For each site, it was delineated drainage sections, ranging from 100 meters to the entire drainage area. For each section, it was calculated landscape metrics for generic land-use types, and it was also calculated the Spearman's rank correlation coefficient, between each metric in each scale with IPtIN. The preliminary analysis of results led to understand during the winter edge length and number of patches of artificial surfaces revealed a negative impact. Variables such as connectance of agricultural land use patches only revealed a negative influence during summer, in a short-range spatial extent. The contrast between agricultural land uses with forested and with artificial areas was the metric with a notable effect, since maximum correlations were achieved for the contrast between forested and agriculture, and minimum in the contrast between agriculture with artificial areas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it