AWARE characterization factor samples
Bibliographic record
Abstract
Files contain 4999 samples of AWARE characterization factors, as well as sampled independent data used in their calculations and selected intermediate results. AWARE is a consensus-based method development to assess water use in LCA. It was developed by the WULCA UNEP/SETAC working group. Its characterization factors represent the relative Available WAter REmaining per area in a watershed, after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived. The code used to generate the samples can be found here: https://github.com/PascalLesage/aware_cf_calculator/ The following datasets are supplied: 1) AWARE_characterization_factor_samples.zip Actual characterization factors resulting from the Monte Carlo Simulation. Contains 4 zip files: * monthly_cf.zip: contains 116,484 arrays of 4999 monthly characterization factor samples for each of 9707 watershed and for each month, in csv format. Names are cf_ _ .csv, where is the watershed id and is the first three letters of the month ('jan', 'feb', etc.). * average_agri_cf.zip: contains 9707 arrays of 4999 annual average, agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_agri_ .csv. * average_non_agri_cf.zip: contains 9707 arrays of 4999 annual average, non-agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_non_agri_ .csv. * average_unknown_cf.zip: contains 9707 arrays of 4999 annual average, unspecified use, characterization factor samples for each watershed, in csv format. Names are cf_average_unknown_ .csv.. 2) AWARE_base_data.xlsx Excel file with the deterministic data, per watershed and per month, for each of the independent variables used in the calculation of AWARE characterization factors. Specifically, it includes: Monthly irrigation Description: irrigation water, per month, per basin Unit: m3/month Location in Excel doc: Irrigation File name once imported: irrigation.pickle table shape: (11050, 12) Non-irrigation hwc: electricity, domestic, livestock, manufacturing Description: non-irrigation uses of water Unit: m3/year Location in Excel doc: hwc_non_irrigation File name once imported: electricity.pickle, domestic.pickle, livestock.pickle, manufacturing.pickle table shape: 3 x (11050,) avail_delta Description: Difference between "pristine" natural availability reported in PastorXNatAvail and natural availability calculated from "Actual availability as received from WaterGap - after human consumption" (Avail!W:AH) plus HWC. This should be added to calculated water availability to get the water availability used for the calculation of EWR Unit: m3/month Location in Excel doc: avail_delta File name once imported: avail_delta.pickle table shape: (11050, 12) avail_net Description: Actual availability as received from WaterGap - after human consumption Unit: m3/month Location in Excel doc: avail_net File name once imported: avail_net.pickle table shape: (11050, 12) pastor Description: fraction of PRISTINE water availability that should be reserved for environment Unit: unitless Location in Excel doc: pastor File name once imported: pastor.pickle table shape: (11050, 12) area Description: area Unit: m2 Location in Excel doc: area File name once imported: area.pickle table shape: (11050,) It also includes: * information on the distributions used for each variable (uncertainty tab) * two filters used to exclude watersheds that are either in Greenland (polar filter) or without data from the Pastor et al. (2014) method (122 cells), representing small coastal cells with no direct overlap (pastor filter). (filters tab) 3) independent_variable_samples.zip Samples for each of the independent variables used in the calculation of characterization factors. Only random variables are contained. For all watershed or watershed-months without samples, the Monte Carlo simulation used the deterministic values found in the AWARE_base_data.xlsx file. The files are in csv format. The first column contains the watershed id (BAS34S_ID) if the data is annual or the (BAS34S_ID, month) for data with a monthly resolution. the other 4999 columns contain the sampled data. The names of the files are . 4) intermediate_variables.zip Contains results of intermediate calculations, used in the calculation of characterization factors. The zip file contains 3 zip files: * AMD_world_over_AMD_i.zip: contains 116,484 arrays (for each watershed-month) of 4999 calculated values of the ratio between the AMD (Availability Minus Demand) for the watershed-month and AMD_glo, the world weighted AMD average. Format is csv. * AMD_world.zip: contains one array of 4999 calculated values of the world average AMD. Format is csv. * HWC.zip: contains 116,484 arrays (for each watershed-month) of 4999 calculated values of the total Human Water Consumption. Format is csv. 5) watershedBAS34S_ID.zip Contains the GIS files to link the watershed ids (BAS34S_ID) to actual spatial data.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.084 |
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".