Datasets for manuscript: Phosphorus recovery in municipal wastewater and socioeconomic impacts in Canada and the United States
Bibliographic record
Abstract
The datasets contain the computer code and data required to determine the cost and economic impacts of phosphorus recovery from municipal wastewater in Canada and the United States. The datasets supply data to (i) calculate the efficiency and cost of phosphorus recovery from the aqueous phase of digestate and sewage sludge for wastewater resource recovery facilities (WRRFs) as shown in Figure 1; (ii) estimate the average annual per capita phosphorus recovery cost and the household affordability index (HAI) across the second-level territory divisions (census divisions (Canada) and counties (United States)) when excluding and including the offset cost derived from avoiding potential environmental damage caused by phosphorus releases as shown in Figure 2; (iii) supply the distribution of population in urban and rural areas, the treatment level of the WRRFs, and the phosphorus recovery points as a function of the WRRF scale in the studied regions of Canada and the United States as shown in Figure 3; and (iv) describe the distribution of the average phosphorus recovery cost, annual per capita phosphorus recovery costs, and the HAI per studied regions as shown in Figure 4. Data describing the WRRFs’ location and characteristics across the studied regions of Canada and the United States are retrieved from the HydroWASTE database (https://www.hydrosheds.org/products/hydrowaste), including their spatial coordinates, treatment level, treatment design capacity, and population served. The HydroWASTE database reports the WRRF treatment level as primary, secondary, and advanced treatment. Since the U.S. Environmental Protection Agency does not define numeric nutrient water quality criteria for secondary wastewater treatment effluents, we consider that only the WRRFs with advanced treatments have specific processes for removing phosphorus from the liquid effluent. To perform the analysis at the second-level divisions, data on total population, distribution of population in urban and rural areas, total income, and average annual income per capita are retrieved at the census division and county level for Canada and the United States, respectively. Data for the year 2020 is considered since it is the most recent information available for both countries. The first-level divisions level corresponds to census divisions of the United States, which provide territorial divisions similar in terms of development, demographic characteristics, and economic activities, being extensively used for collecting and analyzing data throughout the United States. A table of the states included in each United States census division can be found in the Supplementary Information file. The equivalent of the United States census divisions for Canada is the Canadian provinces and territories, although it must be noted that, unlike the case of the United States, their definition is guided by administrative and political considerations instead of statistical criteria.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 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".