Data and codes for Lessons learned from a detailed exploration of APEX as a tool to represent corn residue management and cover crops
Notice bibliographique
Résumé
Code used to generate information used in Arnillas et al. (2025) "Lessons learned from a detailed exploration of APEX as a tool to represent corn residue management and cover crops", the information generated, and the codes to process and visualize it. The codes have not been tested independently, hence, edits will be necessary. For some codes, it may be necessary to download apex_functions_2.R from https://github.com/carlos-arnillas/apex_io/tree/main. apex_functions_2.R is a library of functions developed during this project to support I/O of the several text files used by APEX. winter_crops_test_adj4.R Code that run the simulation as a set of modifications of the input files in run1wc.zip controlled by the scenarios stored in winter_crops_test5b.RData. winter_crops_test_adj4_detailed.R Code that reads the data stored in winter_crops_test5b.RData and generate several figures that support the analyses reported in the manuscript and several otherm figures. run1wc.zip Input file with the descriptors of soil, crops, weather, crop management, and several input parameters required by APEX. winter_crops_test5b.RData File containing all the information generated to test APEX in our study case and the variables describing the scenarios tested and the parameters of the simulation. lres List of results of each simulation. Each simulation is stored as an element of the list. The name of the element correspond to the name column in the scenarios table. The results of each scenario is stored in several tables. The structure and meaning of the following tables correspond to the structure and meaning of the tables as described in the APEX Theoretical Documentation (https://epicapex.tamu.edu/manuals-and-publications/): sao sad dgz dhy dgn msa swn dws asa wss awp dcn acn aws mws mgz The following tables have been added to report complementary information: out_txt: text showing errors and other messages. sad1: same information from than the one stored in SAD, but with crop information listed in long instead of wide format. cvf: table reporting intermediate values necesary to estimate the cover factor (CVF). cvrs: table reporting intermediate values necesary to estimate the above ground crop residue (CVRS). uw: table reporting intermediate values necesary to estimate daily water uptake by soil layer (UW). rwt: table reporting intermediate values necesary to estimate the root weight by soil layer (RWT). scenarios Table describing the conditions used to run each of the scenarios (when relevant, the column names match the correspoing variable being modified in the APEX simulation). Relevant variables used in this simulation are: CPNM: Code of the crop used as cover crop in the simulation. Values: NA means no cover crop, CLVR: Red clover, COAT: Oats, RYE: Rye. ORHI: Values describing the fraction of crop biomass removed from the main crop (corn) after harvesting the grains. weather: Simulation using a repetitive pattern with always the same weather year after year (s, static) or an scenario in which after certain year the weather pattern changes (m, mixed). CNUM: Numeric code representing the crop being used as cover crop. Values: NA means no cover crop, 2397: Red clover, 2363: Oats, 2364: Rye. HU: Heat units of the corresponding cover crop (used to describe the crop growth pattern). SeedDensity: Number seeds added by the model during the planting operation of the cover crop. name: Name of the scenario. The scenario is composed by the soil code (always "O"), and the values from the columns ORHI, CPNM, and weather. CPNMf: A factor (sensu R) created from CPNM to facilitate the presentation of results. ORHIf: A factor (sensu R) created from ORHI to facilitate the presentation of results. responses Table describing the several response variables generated by APEX (including the ones created for this project). var_code: Code name of the variable. Description: Description of the variable. SAD: Is the variable present in the SAD file? ACY: Is the variable present in the ACY file? ASA: Is the variable present in the ASA file? DGN: Is the variable present in the DGN file? DHY: Is the variable present in the DHY file? MSA: Is the variable present in the MSA file? Any: Is the variable present in any of the previous files? Group: Does the variable is important to describe any of these topics (Plant growth, Nitrogen, Phosphorus, Water dynamics, Erosion, or Soil)? Count (not used) units: Unit of the variable. var_number: Does the variable correspond to any of the variables reported in table X of the APEX user manual? Daily: Is the variable reported on a daily time scale? Extension: Extensions of the files in which the variable can be found. PrintID: Codes of the files in which the variable can be found. main_out (not used) Boundary (not used) folders Internal variable describing where to find the information needed to prepare the simulation. opc Table resembling the operation file used APEX to describe the management implemented in any given field. Some values in there are replaced before running the simulation (see file winter_crops_test-adj4.R). run_fd Folder name of the current simulation.
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 | 0,005 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,206 | 0,088 |
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 ».