AWARE characterization factor samples
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,114 | 0,084 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».