A global database of chlorophyll and water chemistry in freshwater lakes
Bibliographic record
Abstract
Chlorophyll is an important variable used to assess water quality in freshwater lakes around the globe. Using a systematic review of the peer-reviewed literature and online repositories, we compiled a database of chlorophyll values. When available, we also aggregated data on lake morphology and water chemistry. Over 3000 published manuscripts were reviewed and 15 online datasets. We obtained 24,483 unique survey in 9625 lakes and 72 countries. Every survey instance had chlorophyll values, and when available other water chemistry variables such as total phosphorus, total nitrogen, dissoved organic carbon, and dissolved oxygen. Within this database, there are files that correspond to the studies that were examined, lake morphology, water chemistry, chlorophyll concentration, and lake information (e.g. location, country, name). The geospatial coordinates that are supply allow for inclusion of variables with raster data such as climate projections, land use, and topography. This dataset can be used for improving our understanding of freshwater equality in response to global change and for management to improve water quality
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".