Sentence examples for maximum layer depth from inspiring English sources
Equation 16 gives the maximum layer depth for which the oxygen concentration at the bottom does not drop below C min ∗.
In the process of calculating a time series of active layer thickness, if the active layer thickness of the prescribed grid cells exceeds the maximum soil layer depth of the climate model in a certain future year, this year and the ensuing years were eliminated.
In response to the need for predicted chlorophyll concentrations in modeled climate scenarios, Sarmiento et al. [22] predict annual mean log chlorophyll using a linear regression against temperature, salinity, length of the growing season, and the maximum winter mixed layer depth over 33 biogeochemical provinces.
The deep chlorophyll maximum layer (DCML) shoaled from a depth of about 130 m in the outer regions of the eddy to about 60 m in the center.
The model results reveal that the maximum thawing depth of the active layer in summer gradually decreases along with the increase of the soil organic layer depth.
Poor night-time atmospheric mixing, lower mixed layer depths, biospheric respiration, and continued emissions from mobile and fixed anthropogenic sources, account for the night-time maxima in CO2 concentrations.
② The model predicted an increase of maximum active layer thawing depth from today 150 cm to about 350 cm as a result of a 4 °C warming and a talik formation at the top of the permafrost as a result of a 6 °C warming.
The results also showed that the reinforced layers achieved maximum rut depth reduction compared to unreinforced section.
Table 1 Optimized model hyper-parameters Algorithm Parameter Range RF The number of active attributes SVM C 2-5 to 215 γ 2-15 to 23 ANN The number of hidden neurons (one-layer) CvBoost Maximum branching depth 1 to 20 The number of trees 1 to 1000 PLS The number of components 1 to nAttr-1 Model hyper-parameters being optimized by default and their corresponding ranges.
Its seemingly complex structure (typical incarnations of which consist of over 100 layers with a maximum depth of over 20 parameter layers), is based on two insights: the Hebbian principle and scale invariance.
