In the forests of Madhya Pradesh, there is wide-spread damage, due to borer. In which tree specie has the damage occurred? ?->(Show Answer!)

1. In the forests of Madhya Pradesh, there is wide-spread damage, due to borer. In which tree specie has the damage occurred?

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MCQ->In the forests of Madhya Pradesh, there is wide-spread damage, due to borer. In which tree specie has the damage occurred?....

MCQ-> Read the passage carefully and answer the questions given below it. Certain words/phrases have been given in bold to help you locate them while answering some of the questions. Long time ago, in a forest, there lived a young antelope. He was fond of the fruits of a particular tree. In a village bordering the forest, there lived a hunter who captured and killed antelopes for various reasons. He used to set traps for animals under fruitbearing trees. When the animal came to eat the fruit, it would be caught in the trap. He would then take it away and kill it for its meat. One day, while visiting the forest in search of game, the hunter happened to see the antelope under its favourite tree, eating fruit. He was delighted. ‘What a big, plump antelope!’ he thought. ‘I must catch him. I will get a lot of money from selling his meat.’ Thereafter, for many days, the hunter kept track of the antelope’s movements. He realised that the antelope was remarkably vigilant and fleet footed animal that it would be virtually impossible for him to track him down. However, he had a weakness for that particular tree. The crafty concluded that he could use this weakness to capture him. Early one morning, the hunter entered the forest with some logs of wood. He climbed the tree and put up a machan (platform used by hunters) on one of its branches by tying the logs together. Having set his trap at the foot of the tree, he then took up position on the machan and waited for the antelope. He strewed a lot of iy ,ovef mrui bts eo rn2thoeig6round beneath the 11.004.3, tree to conceal the trap and lure the antelope. Soon, the antelope came strolling along. He was very hungry and was eagerly looking forward to his usual breakfast of delicious ripe fruits. On the treetop, the hunter, having sighted him, sat with bated breath, willing him to come closer and step into his trap. However, the antelope was no fool. As he neared the tree he stopped short. The number of fruits lying under the tree seemed considerably more than usual. Surely, something was amiss, decided the antelope. He paused just out of reach of the tree and carefully began examining the ground. Now, he saw what distinctly looked like a human footprint. Without going closer, he looked suspiciously at the tree. The hunter was well hidden in its thick foliage, nevertheless the antelope, on close scrutiny, was now sure that his suspicions had not been unfounded. He could see a corner of the machan peeping out of the leaves. Meanwhile the hunter was getting desperate. Suddenly, he had a brainwave. Let me try throwing some fruit at him,’ he thought. So the hunter plucked some choice fruits and hurled them in the direction of the antelope. Alas, instead of luring him closer, it only confirmed his fears! Raising his voice, he spoke in the direction of the tree —”Listen, my dear tree, until now you have always dropped your fruits on the earth. Today, you have started throwing them at me! This is the most unlikely action of yours and I’m not sure I like the change! Since you have changed your habits, I too will change mine. I will get my fruits from a different tree from now onone that still acts like a tree!’ The hunter realised that the antelope had outsmarted him with his cleverness. Parting the leaves to reveal himself, he I grabbed his javelin and flung it wildly at the antelope. But the clever antelope was well prepared for any such action on his part. Giving a saucy chuckle, he leapt nimbly out of the harm’s way.As mentioned in the story, which of the following can be said about the hunter ?
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MCQ-> Read the passage carefully and answer the given questionsThe complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors. . . . The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. . . .Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of enquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail. What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact. It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules. Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behaviour. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases - those that the current forest gets wrong. This ensures even more diversity and accurate forests.Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to breakthroughs.Which of the following conditions, if true, would invalidate the passage’s main argument?
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