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151 to 160 of 3,322 Results
Tab-Delimited - 1.8 MB - MD5: b56570fc695ece2feb1d58627985846c
MIR spectra of the manure standards using Alpha ZnSe instrument
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,...
Tab-Delimited - 638.4 KB - MD5: 125cbbe75fca81717a8b1985491578c7
Plot properties
Tab-Delimited - 216.5 KB - MD5: 8e6b0d885a4cbfa6913a3be1c9f482ec
Description of codes
Tab-Delimited - 1.5 KB - MD5: 55d359a61b794353505b676f30b498c0
Predicted soil variables description
Tab-Delimited - 253.4 KB - MD5: 685647fcd9b3b120bd8a07f81a00d7fa
Predicted soil properties
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