The single most important chemical weathering agent is Carbon dioxide.
Weathering refers to the process that change the physical and chemical character of rock at or near the surface. Weathering has a dramatic impact on the composition of Earth's atmosphere. Chemical weathering removes carbon dioxide from the atmosphere, allowing it to be transformed into limestone and stored in the crust. Without chemical weathering, the elevated levels of carbon dioxide in the atmosphere would have long made Earth too hot to sustain life.
Answer:
d
Explanation:
sugar molecules are being broken down
Answer:
2Li(s) + 2H₂O(ℓ) ⟶ 2Li⁺(aq) + 2OH⁻(aq) + H₂(g)
Explanation:
An ionic equation uses the symbols (aq) [aqueous] to indicate molecules and ions that are soluble in water, (s) [solid] to indicate insoluble solids, and (ℓ) to indicate substances (usually water) in the liquid state.
In this reaction, solid lithium reacts with liquid water to form soluble lithium hydroxide and gaseous hydrogen
.
1. Molecular equation
2Li(s) + 2H₂O(ℓ) ⟶ 2LiOH(aq) + H₂(g)
2. Ionic equation
Lithium hydroxide is a soluble ionic compound, so we write it as hydrated ions.
2Li(s) + 2H₂O(ℓ) ⟶ 2Li⁺(aq) + 2OH⁻(aq) + H₂(g)
Correct option:
Entropy is used to calculate information gain.
What is entropy?
- Entropy is the measure of data's uncertainty or randomness; the greater the randomness, the higher the entropy. Entropy is used by information gain to make choices. Information will increase if the entropy decreases.
- Decision trees and random forests leverage information gained to determine the appropriate split. Therefore, the split will be better and the entropy will lower the more information gained.
- Information gain is calculated by comparing the entropy of a dataset before and after a split.
- Entropy is a way to quantify data uncertainty. The goal is to maximize information gain while minimizing entropy. The method prioritizes the feature with the most information, which is utilized to train the model.
- Entropy is actually used when you use information gain.
Learn more about entropy here,
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