Hail pieces of ice that form and fall from clouds instead of snow or rain has always been a problem for farmers in some areas of the United States Hail pellets can fall with great force and destroy crops in the field Over the last few decades a method of

Essay topics:

Hail—pieces of ice that form and fall from clouds instead of snow or rain—has always been a problem for farmers in some areas of the United States. Hail pellets can fall with great force and destroy crops in the field. Over the last few decades, a method of reducing hail, called “cloud seeding,” has been tried. In cloud seeding, the chemical silver iodide is sprayed on storm clouds from an airplane. This makes the clouds produce harmless rain or snow instead of hail. Several pieces of evidence suggest that cloud seeding has been effective in protecting crops from hail.

Laboratory experiments

Experiments in the laboratory support the idea that cloud seeding is effective. Hail usually forms in water vapor that is close to the freezing point. However, when experimenters added silver iodide to cold water vapor in the laboratory, they often observed light snow forming instead of hail pellets.

Evidence from Asia

There is evidence about the effectiveness of cloud seeding from several countries around the world. In some Asian countries, for example, cloud seeding has been successfully used to control precipitation in urban areas. These positive results suggest that cloud seeding should also be effective in protect

The reading and the lecture are both about cloud seeding which is a method for reducing problems from Hail. The author of reading believes that this method can be effective to prevent damage made by hail pellets. However, the lecturer casts doubt on the claims made in the article. She think that the effectiveness of this method is unclear.

First of all, the reading posits that laboratory experiments shows that cloud seeding is a useful way to change hail to light snow. Nevertheless, this point is challenged by the professor. She mentions this may be happen under laboratory condition but in real world this could be prevent producing of snow and rain. Furthermore, the professor states that this could be led to drought and as a result of this method water shortage can cause more damage to crops.

Second, the author claims that cloud seeding method used in several countries and the result was positive. It is argued that in some Asian countries cloud seeding has been applied to control precipitation in urban regions. This argument is rebutted by the lecturer. She claims in urban areas air pollotin is responsable for positive result of using this method. She states that cars and industries in urban area are the reason for air pollotion but in rural and farmland the air is clear and without air pollotion this is not possible to have same result as urban regions.

Finally, the author mentions that local studies also suggest that cloud seeding is valuble method. The author gives an example of a study which conducted in farming region in the central United stated and the study support the efectivness of cloud seeding method. The lecturer, on the other hand, refuses this claim bay saying that the local studies result is not convincing. She says that decrese in the damage from hail is not becuase of cloud seeding method. She puts forth the idea that reduction of hail is because of natural variation of climate which also happened in other regions.

In sum, all three reasons mentioned in the reading are effectivelly challenged by the lecturer.

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Comments

Grammar and spelling errors:
Line 1, column 287, Rule ID: HE_VERB_AGR[1]
Message: The pronoun 'She' must be used with a third-person verb: 'thinks'.
Suggestion: thinks
... on the claims made in the article. She think that the effectiveness of this method i...
^^^^^

Transition Words or Phrases used:
also, but, finally, first, furthermore, however, may, nevertheless, second, so, as a result, first of all, on the other hand

Attributes: Values AverageValues Percentages(Values/AverageValues)% => Comments

Performance on Part of Speech:
To be verbs : 22.0 10.4613686534 210% => Less to be verbs wanted.
Auxiliary verbs: 5.0 5.04856512141 99% => OK
Conjunction : 10.0 7.30242825607 137% => OK
Relative clauses : 16.0 12.0772626932 132% => OK
Pronoun: 31.0 22.412803532 138% => Less pronouns wanted
Preposition: 44.0 30.3222958057 145% => OK
Nominalization: 7.0 5.01324503311 140% => OK

Performance on vocabulary words:
No of characters: 1728.0 1373.03311258 126% => OK
No of words: 351.0 270.72406181 130% => OK
Chars per words: 4.92307692308 5.08290768461 97% => OK
Fourth root words length: 4.32839392791 4.04702891845 107% => OK
Word Length SD: 2.46671990803 2.5805825403 96% => OK
Unique words: 168.0 145.348785872 116% => OK
Unique words percentage: 0.478632478632 0.540411800872 89% => More unique words wanted or less content wanted.
syllable_count: 520.2 419.366225166 124% => OK
avg_syllables_per_word: 1.5 1.55342163355 97% => OK

A sentence (or a clause, phrase) starts by:
Pronoun: 9.0 3.25607064018 276% => Less pronouns wanted as sentence beginning.
Article: 9.0 8.23620309051 109% => OK
Subordination: 0.0 1.25165562914 0% => More adverbial clause wanted.
Conjunction: 0.0 1.51434878587 0% => OK
Preposition: 2.0 2.5761589404 78% => OK

Performance on sentences:
How many sentences: 19.0 13.0662251656 145% => OK
Sentence length: 18.0 21.2450331126 85% => The Avg. Sentence Length is relatively short.
Sentence length SD: 38.5436400508 49.2860985944 78% => OK
Chars per sentence: 90.9473684211 110.228320801 83% => OK
Words per sentence: 18.4736842105 21.698381199 85% => OK
Discourse Markers: 6.52631578947 7.06452816374 92% => OK
Paragraphs: 5.0 4.09492273731 122% => OK
Language errors: 1.0 4.19205298013 24% => OK
Sentences with positive sentiment : 9.0 4.33554083885 208% => Less positive sentences wanted.
Sentences with negative sentiment : 9.0 4.45695364238 202% => Less negative sentences wanted.
Sentences with neutral sentiment: 1.0 4.27373068433 23% => More facts, knowledge or examples wanted.
What are sentences with positive/Negative/neutral sentiment?

Coherence and Cohesion:
Essay topic to essay body coherence: 0.164066704152 0.272083759551 60% => OK
Sentence topic coherence: 0.0574212934188 0.0996497079465 58% => OK
Sentence topic coherence SD: 0.051054276785 0.0662205650399 77% => OK
Paragraph topic coherence: 0.0881848515636 0.162205337803 54% => OK
Paragraph topic coherence SD: 0.0501027871242 0.0443174109184 113% => OK

Essay readability:
automated_readability_index: 11.0 13.3589403974 82% => Automated_readability_index is low.
flesch_reading_ease: 61.67 53.8541721854 115% => OK
smog_index: 3.1 5.55761589404 56% => Smog_index is low.
flesch_kincaid_grade: 9.1 11.0289183223 83% => OK
coleman_liau_index: 11.26 12.2367328918 92% => OK
dale_chall_readability_score: 8.04 8.42419426049 95% => OK
difficult_words: 78.0 63.6247240618 123% => OK
linsear_write_formula: 7.0 10.7273730684 65% => OK
gunning_fog: 9.2 10.498013245 88% => OK
text_standard: 9.0 11.2008830022 80% => OK
What are above readability scores?

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Rates: 73.3333333333 out of 100
Scores by essay e-grader: 22.0 Out of 30
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Note: the e-grader does NOT examine the meaning of words and ideas. VIP users will receive further evaluations by advanced module of e-grader and human graders.