Aug 2, 2021 -
Climate Information Services Research Initiative (CISRI) Evaluation in Senegal: Baseline Survey data
Unknown - 78.7 KB - MD5: 9fe0bc52c98259f1b9554f8b71dc7686
Rmarkdown code file for computing indicators |
Unknown - 81.6 MB - MD5: a6b8d8504ce61253cae2079c2f3885ed
Using forest regression machine learning models, excellent calibration functions could be established using the MIR spectra. Forest models for there DRIFT-MIR spectra were run using 1500 trees with 200 resampling events using k-fold cross validation over 25 iterations based on th... |
Unknown - 29.3 MB - MD5: ac2c20646882bb5b387f26ed9ed53cae
Extreme gradient boosting machine learning (XGBoost) models were used with data from
DRIFT-MIR spectroscopy and these differ from forest in that trees can be weighted differently, have fixed depths, and different resembling. XGBoost (0.82.1) models were run using 400 rounds with... |
Unknown - 6.8 MB - MD5: 2a26970f2271fee45954c97ad6bd68bd
Forest models for pXRF spectra from the instrument serial number 900F4473 Light Calibration (10 kV, 70 μA and 90 seconds, No filter. Elemental Range: Na, Mg, Al, Si, P,
S, K, Ca, Ti, Cr, Mn, and Fe) were run using 1500 trees with 200
resampling events using k-fold cross valida... |
Unknown - 7.9 MB - MD5: 4c32bb4d01fe758d0a520fe679090403
Extreme gradient boosting machine learning (XGBoost) models were used with data from portable XRF instrument serial number 900F4473 for the Manure Light Calibration (10 kV, 70 μA and 90 seconds, No filter. Elemental Range: Na, Mg, Al, Si, P,
S, K, Ca, Ti, Cr, Mn, and Fe.
(ii)... |
Unknown - 3.5 MB - MD5: b9f0bcbf4a91de38b325a2644dcdc673
Extreme gradient boosting machine learning (XGBoost) models were used with data from portable XRF instrument serial number 900F4473 for the Manure Trace Calibration (35 kV, 35 μA and 90 seconds, Filter- Cu 75um:Ti 25um:Al 200um.
Elemental Range: K, Ca, Ti, Cr, Mn, Fe, Co, Ni,... |
Nov 26, 2020 -
Farm inventory surveys in Rwanda: Birds data
Unknown - 257.0 KB - MD5: 533e5082bd60a4c0ab3c7416389b5a72
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Nov 26, 2020 -
Farm inventory surveys in Rwanda: Computed data files
Unknown - 34.4 KB - MD5: cb2c02606bde2d8b87b8a8a2f52bc0f6
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Nov 26, 2020 -
Farm inventory surveys in Rwanda: Computed data files
Unknown - 80.2 KB - MD5: 460439a26c8c672b0262001777a989a4
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Nov 26, 2020 -
Farm inventory surveys in Rwanda: Computed data files
Unknown - 153.9 KB - MD5: 40e7432e5dffb6a87ec18ad675b46536
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