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Record W2951790018 · doi:10.1594/pangaea.900318

Global manure phosphorus, human population density, cropland extent, livestock density, and nation-level phosphorus fertilizer use (circa 2010)

2019· dataset· en· W2951790018 on OpenAlexaboutno aff
Stephen M. Powers

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphorusManureLivestockEnvironmental scienceFertilizerPopulationPopulation densityAgronomyGeographyForestryBiologyChemistryEnvironmental health

Abstract

fetched live from OpenAlex

Detailed methods can be found in the publication, and highlights are provided below. The following original data sources were aggregated/disaggregated to a common hexagonal grid (cell size 290 km2, mean internode spacing 18.3 km): Gridded Livestock of the World (GLW 2), doi:10.1371/journal.pone.0096084, reporting year 2006, resolution 3 arcminutes (~5 km2 at equator); Gridded Population of the World (GPWv4), doi:10.7927/H4HX19NJ, reporting year 2010, resolution 30 arcseconds (~1 km at equator); GlobCover 2009, doi:10.1594/PANGAEA.787668, reporting year 2009, resolution 300m; FAOSTAT Fertilizers by Nutrient dataset (downloaded on 26 Feb 2018), http://www.fao.org/faostat/en/#data/RFN/metadata, reporting years 2002-2014, resolution national. ---Subnational methods and calculations Livestock densities, human population density, and cropland extent were summarized for each grid cell in a global hexagonal grid. This grid had consistent grid cell areas across latitudes, and was generated using the dggrid package (Barnes, 2016; Sahr, 2011) in the platform R (R Core Team, 2016). In the finer hexagonal grid, each grid cell had a mean area of 290 km2 and a mean internode spacing of 18.3 km. In the coarser grid, each grid cell had a mean side length of 95 km (mean hexagon area of 23,300 km2, mean internode spacing of 165 km), which was large enough to encompass megacities such as London and Paris along with peri-urban areas, but small enough to maintain subnational resolution in relatively small nations. For a minority of hexagonal grid cells, slight deviations in the dimensions were mathematically necessary to avoid overlapping cells and gaps over the world's surface (Barnes, 2016). Total manure P production in each grid cell was calculated by summing the contributions from each animal type, using animal-specific and nation-specific P excretion factors from Bouwman et al. (2017). For cattle we used 16.6 kg P per head yr-1 in Canada, USA, and Japan, 13.1 kg P per head yr-1 in the other OECD countries, and 8.75 kg P per head yr-1 in the remaining countries (Bouwman et al. 2017). For other animals we used 1.8 kg P per head yr-1 for pigs, 0.1 kg P per head yr-1 for chickens, 1.5 kg P per head yr-1 for sheep and goats for all countries (Bouwman et al. 2017). Cells with zero cropland extent were excluded from the analysis (and thus also gridcelldata.csv). --National methods and calculations We used nation-level P fertilizer data from FAOSTAT including import, export, agricultural use, and production for the most recent available years (2002-2014). FAOSTAT data were downloaded on 26 Feb 2018. Fertilizer data are reported annually, and we took the nation-specific means for each budgetary term over two different five year intervals (2010-2014, 2002-2006); these years deliberately exclude the global food crisis of 2007/2008 when the global phosphate rock price spiked by 400% (Chowdhury et al., 2017). A small number of countries had data gap years, requiring that the mean be calculated over fewer years. Import ratios, an indicator of fertilizer P import dependency, were calculated as net import : consumption, where net import = import - export. Recent fertilizer P consumption trends were summarized by calculating a consumption ratio of the 2010s to 2000s (2010-2014:2002-2006). Calculations involving P import ratios and consumption trends were conducted directly on FAO data, prior to disaggregation within the global grid. In cases where grid cells overlapped multiple countries, the nation representing the largest share of the grid cell was assigned to the whole cell using administrative data from Natural Earth. A minority of nations lacked P import or P consumption data and were excluded from P import ratio calculations. Nations that lacked P export data were assumed to have zero gross P export in these calculations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.127
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.014
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0360.014

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.

Opus teacher head0.053
GPT teacher head0.261
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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