Relationships of total phosphorus and chlorophyll in lakes worldwide
Bibliographic record
Abstract
Abstract Lakes around the world are sensitive to water quality degradation and eutrophication through increases in primary production. Understanding the drivers of primary production has been a fundamental question in limnology since its early days. Here, we conducted a systematic review to develop a dataset of water chemistry and lake morphometry for 3874 lakes distributed across 47 countries around the world to answer: (1) What is the global relationship between chlorophyll a (Chl a ) and total phosphorus (TP) in lakes? (2) Are there inflection points at which the TP–Chl a relationship is no longer linear? and (3) What explains the inflection points and nonlinearities in the TP–Chl a relationship? We found that a sigmoidal relationship between TP and Chl a explained 44% of the variation. We also found physical characteristics of the lake mediated the TP–Chl a relationship such as mean depth, Secchi depth, and elevation. The nonlinear segments of this relationship best described lakes located in very cold (mean annual temperature = −10°C) and hot (> 25°C) climates, which also dominated the high and low ends of TP concentrations, respectively. A positive linear TP–Chl a relationship existed at intermediate concentrations of TP (0.004–0.23 mg L −1 ). A high degree of variability in Chl a exists between lakes at similar TP levels, highlighting the difficulty in simply decreasing nutrient inputs to manage eutrophication in lakes worldwide. Moreover, as global temperatures continue to rise, the Chl a –TP relationship in lakes located in very cold or warm temperate regions of the world may shift in response to these warmer temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".