Identifying the Influence of Land Cover and Human Population on Chlorophyll a Concentrations Using a Pseudo-Watershed Analytical Framework
Bibliographic record
Abstract
Increasing agricultural development and urbanization exacerbates the degradation of water quality in vulnerable freshwater systems around the world. Advances in remote sensing and greater availability of open-access data provides a valuable resource for monitoring water quality but harmonizing between databases remains a challenge. Here, we: (i) developed a pseudo-watershed analytical framework to associate freshwater lakes with adjacent land cover and human population data and (ii) applied the framework to quantify the relative influence of land cover and human population on primary production for 9313 lakes from 72 countries. We found that land cover and human population explained 30.2% of the variation in chlorophyll a concentrations worldwide. Chlorophyll a concentrations were highest in regions with higher agricultural activities and human populations. While anthropogenic land cover categories equated to only 4 of the 18 categories, they accounted for 41.5% of the relative explained variation. Applying our pseudo-watershed analytical framework allowed us to quantify the importance of land cover and human population on chlorophyll concentration for over 9000 lakes. However, this framework has broader applicability for any study or monitoring program that requires quantification of lake watersheds.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".