Answer:
The answer is choice (3)
Explanation: Land materials do require more energy than water to raise their temperature by one degree, hence why water gets heated a lot easier than something like wood.
The mass of the iceberg is 71.9 kg
Explanation:
The amount of thermal energy needed to completely melt a substance at its melting point is given by
![Q=\lambda m](https://tex.z-dn.net/?f=Q%3D%5Clambda%20m)
where
is the latent heat of fusion
m is the mass of the substance
In this problem, we have a block of ice at its melting point (zero degrees). The amount of heat given to the block is
![Q=2.40\cdot 10^7 J](https://tex.z-dn.net/?f=Q%3D2.40%5Ccdot%2010%5E7%20J)
And the latent heat of fusion of ice is
![\lambda = 334 J/g](https://tex.z-dn.net/?f=%5Clambda%20%3D%20334%20J%2Fg)
So, we can re-arrange the equation to find m, the amount of ice that will melt:
![m=\frac{Q}{\lambda}=\frac{2.40\cdot 10^7}{334}=71856 g = 71.9 kg](https://tex.z-dn.net/?f=m%3D%5Cfrac%7BQ%7D%7B%5Clambda%7D%3D%5Cfrac%7B2.40%5Ccdot%2010%5E7%7D%7B334%7D%3D71856%20g%20%3D%2071.9%20kg)
Learn more about specific heat:
brainly.com/question/3032746
brainly.com/question/4759369
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Answer:
c. Performs better on training data as the training process proceeds, while performing worse on a held-out test data
Explanation:
An over-fitted model is one that will perform best on training but would fail or do worse on a held-out test data.
Such models are optimum for a just a particular set of data but would grossly failed when extrapolated to some other data set not novel to it.
- Over-fitting a model implies that a model closely corresponds to a set of data but would not perform well with others.
- It is usually as a result of a model adapting the noise and other details of a particular data set and thereby incorporates it.
- This makes it difficult for the model to fit into another data set.