--- title: A robot penguin waddles among real penguins and films them. Why does… — How nature documentaries are made: hid… · tree and pragmatics description: A robot penguin waddles among real penguins and films them. Why does it work? Test 9 of lesson 394 «How nature documentaries are made: hides, camera traps, drones and years of waiting» of the biology course of Mova's primary school, with the dependency tree and pragmatics of the question and of each --- [Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/corpora/questions/biology/394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting/t9/) [home](https://md.mova.elamur.ai/free/en/) / [corpora](https://md.mova.elamur.ai/free/en/corpora/) / [questions](https://md.mova.elamur.ai/free/en/corpora/questions/) / [Biology](https://md.mova.elamur.ai/free/en/corpora/questions/biology/) / [How nature documentaries are made: hid…](https://md.mova.elamur.ai/free/en/corpora/questions/biology/394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting/) / t9 # Test 9 · How nature documentaries are made: hides, camera traps, dr… A robot penguin waddles among real penguins and films them. Why does it work? **Mova's choice:** option 2 · **The right answer:** option 3 · wrong ✗ · **Who acts:** robot penguin, penguin · **What happens:** waddle, work This is test 9 of lesson 394 «How nature documentaries are made: hides, camera traps, drones and years of waiting» of the biology course of Mova's primary school. The page gives the question's syntax tree — a dependency tree in the style of Universal Dependencies — and its pragmatics clause by clause, then the 4 options as the student saw them, each with its tree and pragmatics. Every text is parsed by Mova itself. How the student answered it, with the score of every option and why: [the test at school](https://md.mova.elamur.ai/free/en/school-run/biology/394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting/#t9). ## The question {#question} *Sentence 1:* A robot penguin waddles among real penguins and films them. ```conllu # sent_id = question:biology:394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting:t9:1 # text = A robot penguin waddles among real penguins and films them. 1 A a DET DT Definite=Ind|PronType=Art 3 det _ _ 2 robot robot NOUN NN Number=Sing 3 compound _ _ 3 penguin penguin NOUN NN Number=Sing 4 nsubj _ _ 4 waddles waddle VERB VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 0 root _ _ 5 among among ADP IN _ 7 case _ _ 6 real real ADJ JJ Degree=Pos 7 amod _ _ 7 penguins penguin NOUN NNS Number=Plur 4 obl _ _ 8 and and CCONJ CC _ 9 cc _ _ 9 films film NOUN NNS Number=Plur 7 conj _ _ 10 them they PRON PRP Case=Acc|Number=Plur|Person=3|PronType=Prs 7 nmod _ SpaceAfter=No 11 . . PUNCT . _ 4 punct _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **waddle** | present | a robot penguin | | | | | among real penguins films and films | vn run-51.3.2 «NP V PP.location»: agent=Theme among penguin=Trajectory | One row per clause the reader finds. «not said here»: a role filled from the clause above. «roles by»: a VerbNet class and frame when one fits the verb, else the reader's rules on the tree. *Sentence 2:* Why does it work? ```conllu # sent_id = question:biology:394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting:t9:2 # text = Why does it work? 1 Why why ADV WRB PronType=Int 4 advmod _ _ 2 does do AUX VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 4 aux _ _ 3 it it PRON PRP Case=Nom|Gender=Neut|Number=Sing|Person=3|PronType=Prs 4 nsubj _ _ 4 work work VERB VB VerbForm=Inf 0 root _ SpaceAfter=No 5 ? ? PUNCT . _ 4 punct _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **work** | present · do-support | penguin | | | | | | vn employment-95.3 «NP V»: agent=Agent | ## The options {#options} ### Option 1 {#o1} The robot speaks penguin language ```conllu # sent_id = question:biology:394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting:t9:o1:1 # text = The robot speaks penguin language 1 The the DET DT Definite=Def|PronType=Art 2 det _ _ 2 robot robot NOUN NN Number=Sing 3 nsubj _ _ 3 speaks speak VERB VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 0 root _ _ 4 penguin penguin NOUN NN Number=Sing 5 compound _ _ 5 language language NOUN NN Number=Sing 3 obj _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **speak** *speech: other* | present | the robot | penguin language | | | | | tree rules | ### Option 2 · ← Mova's choice {#o2} Penguins cannot see at all, so they never notice anything new around them ```conllu # sent_id = question:biology:394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting:t9:o2:1 # text = Penguins cannot see at all, so they never notice anything new around them 1 Penguins penguin NOUN NNS Number=Plur 4 nsubj _ _ 2 can can AUX MD VerbForm=Fin 4 aux _ SpaceAfter=No 3 not not PART RB Polarity=Neg 4 advmod _ _ 4 see see VERB VB VerbForm=Inf 0 root _ _ 5 at at ADP IN _ 6 case _ _ 6 all all DET DT PronType=Tot 4 obl _ SpaceAfter=No 7 , , PUNCT , _ 11 punct _ _ 8 so so ADV RB _ 11 advmod _ _ 9 they they PRON PRP Case=Nom|Number=Plur|Person=3|PronType=Prs 11 nsubj _ _ 10 never never ADV RB PronType=Neg 11 advmod _ _ 11 notice notice VERB VBP Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin 4 conj _ _ 12 anything anything PRON NN Number=Sing|PronType=Ind 11 obj _ _ 13 new new ADJ JJ Degree=Pos 12 amod _ _ 14 around around ADP IN _ 15 case _ _ 15 them they PRON PRP Case=Acc|Number=Plur|Person=3|PronType=Prs 11 obl _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **not** **see** | can | Penguins | | | all | | | vn comprehend-87.2-1-1 «NP V»: agent=Experiencer | | 2 | **not** **notice** | present | Penguins *= penguin* | anything | | | | around them *= they* | vn see-30.1-1 «NP V S»: agent=Experiencer anything=Stimulus | ### Option 3 · ✓ the right answer {#o3} Real penguins often ignore a robot that looks and moves a bit like them ```conllu # sent_id = question:biology:394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting:t9:o3:1 # text = Real penguins often ignore a robot that looks and moves a bit like them 1 Real real ADJ JJ Degree=Pos 2 amod _ _ 2 penguins penguin NOUN NNS Number=Plur 4 nsubj _ _ 3 often often ADV RB _ 4 advmod _ _ 4 ignore ignore VERB VBP Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin 0 root _ _ 5 a a DET DT Definite=Ind|PronType=Art 6 det _ _ 6 robot robot NOUN NN Number=Sing 4 obj _ _ 7 that that PRON WDT PronType=Rel 8 nsubj _ _ 8 looks look VERB VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 6 acl:relcl _ _ 9 and and CCONJ CC _ 10 cc _ _ 10 moves move VERB VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 8 conj _ _ 11 a a DET DT Definite=Ind|PronType=Art 12 det _ _ 12 bit bit NOUN NN Number=Sing 10 obl:unmarked _ _ 13 like like ADP IN _ 14 case _ _ 14 them they PRON PRP Case=Acc|Number=Plur|Person=3|PronType=Prs 10 obl _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **ignore** | present | Real penguins | a robot | | | | often *property* | vn neglect-75.1 «NP V NP»: agent=Agent robot=Theme | | 2 | **look** | present | a robot | | | | | | vn stimulus\_subject-30.4 «NP V PP.attribute»: agent=Stimulus | | 3 | **move** | present | a robot *not said here* | a bit | | | | like penguins *= penguin* | vn roll-51.3.1 «NP.theme V»: agent=Theme | ### Option 4 {#o4} Penguins love all machines ```conllu # sent_id = question:biology:394-how-nature-documentaries-are-made-hides-camera-traps-drones-and-years-of-waiting:t9:o4:1 # text = Penguins love all machines 1 Penguins penguin NOUN NNS Number=Plur 2 nsubj _ _ 2 love love VERB VBP Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin 0 root _ _ 3 all all DET DT PronType=Tot 4 det _ _ 4 machines machine NOUN NNS Number=Plur 2 obj _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **love** | present | Penguins | all machines | | | | | vn admire-31.2-1 «NP V NP»: agent=Experiencer machine=Stimulus | **Text:** the tests of Mova's primary school, written for Mova (`world/data/school/`), as the course runner gave them to the student. **Trees:** Mova's own parse — the `en` model on the CPU, when the site is built — not checked by a teacher; they may be wrong. **Frames:** Mova's frames reader (`prag`), each text read on its own. 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