The relationship between maritime phosphate pollution and socioeconomic wellbeing
Bibliographic record
Abstract
Phosphate rock reserves are expected to deplete in the next 50-100 years, with the point of highest phosphorus production predicted to be in 2030. Phosphate, the base of many fertilizers, is a non-renewable resource. Ocean phosphate concentrations provide a good indication of global fertilizer use, since agricultural runoff often contributes to increases in ocean phosphate concentration. This study explores the relationship between the concentration of phosphate in a nation’s maritime borders and the nation’s score on the Social Progress Index. The study aims to link findings with possible approaches to help meet two of the United Nations’ (UN) Sustainable Development Goals: creating sustainable communities, and conserving and sustainably using oceans. Phosphate concentration data were acquired from the National Oceanic and Atmospheric Administration and compared with factors of social welfare from the 2018 Social Progress Index. For each nation on the Social Progress Index, the nation’s score on every factor was separately compared to ocean phosphate concentration data within that nation’s maritime borders, and a linear regression was performed for each comparison. The results indicate countries ranking higher on the Social Progress Index generally have greater ocean phosphate concentrations, suggesting that countries of higher social welfare contribute more to global phosphate use or have greater amounts of fertilizer runoff. The findings should be considered by developed nations to inform decisions around pollution reduction as well as developing nations aiming for sustainable social progress. Both should consider the environmental effects that social progress has or will have on the greater global community, of which the significance to sustainable resource development and environmental protection is invaluable.
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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 | 0.001 | 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 | 0.001 | 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".