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Grade 12: Science subject test

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1. A star’s luminosity stays fixed while observer distance doubles under inverse-square spreading. What follows? Which inference or prediction is best supported?It doubles.It remains identical.The star’s emitted luminosity must drop fourfold.Received intensity becomes one-quarter of the original.

2. Supplied case 6: distance (light-years)=100, using the explicitly named quantities. Travel years equal distance in light-years divided by one light-year per year. What result follows?101 years99 years100 years36500 years

3. A shift estimate is the same size as the supplied calibration uncertainty. What follows? Which inference or prediction is best supported?A small apparent shift guarantees a measured speed.The record cannot establish a precise shift beyond that uncertainty without further evidence.Uncertainty must be ignored to get an exact motion.Every noisy line proves recession.

4. Distance is calculated from a stated luminosity model whose assumptions may be wrong. What follows? Which inference or prediction is best supported?The distance inference remains conditional on those assumptions and data.Calculated distance is guaranteed exact.The model directly measures every kilometer without observations.Assumptions matter only for arithmetic errors.

5. Supplied case 6: incoming energy (model units)=245; outgoing energy (same units)=248, using the explicitly named quantities. Net input equals incoming supplied energy minus outgoing supplied energy. What result follows?-2 model energy units-4 model energy units-3 model energy units493 model energy units

6. Initial warming triggers a response that reduces the original warming in the supplied loop. What follows? Which inference or prediction is best supported?Negative means morally bad.The loop is opposing negative feedback, not necessarily complete cancellation.Every negative feedback makes temperature exactly unchanged.It must amplify warming.

7. A shade plan reduces people’s stated heat exposure without changing emissions. What primary pathway fits? Which inference or prediction is best supported?Adaptation reduces exposure while mitigation would address the driver.It necessarily stops global greenhouse emissions.Exposure reduction proves zero heat hazard.Adaptation and mitigation are identical in every case.

8. A model projects an outcome interval under emissions scenario S. What report is warranted? Which inference or prediction is best supported?A guaranteed exact future value regardless of scenario.The interval proves the scenario must occur.Only the best endpoint should be published.A conditional range under S, retaining model uncertainty.

9. Supplied case 6: model emissions (kg)=300; delivered service units=100, using the explicitly named quantities. Emissions intensity equals model kilograms of emissions divided by delivered service units. What result follows?2 kg/service unit3 kg/service unit30000 kg/service unit4 kg/service unit

10. Supplied case 6: intensity (kg/service unit)=5; service units=20, using the explicitly named quantities. Total model emissions equal kilograms per service unit multiplied by service units. What result follows?100 kg0.25 kg101 kg99 kg

11. A disposal stage is omitted from a claim about total lifecycle impacts. What follows? Which inference or prediction is best supported?An omitted stage has zero impact automatically.Operation data proves all disposal outcomes.Adding a stage creates real impact retroactively.The total claim exceeds the measured boundary until that relevant stage is included.

12. A claimed lower-impact option omits end-of-life data and doubles service volume. What must be checked? Which inference or prediction is best supported?Disposal is irrelevant to every boundary.A missing stage can be marked passed.Include end-of-life and recalculate total impact at the stated volume.Lower intensity guarantees lower total at any volume.

13. Supplied case 6: reliability rating=5; efficiency rating=4, using the explicitly named quantities. Score equals two times reliability rating plus efficiency rating. What result follows?14 score points13 score points15 score points28 score points

14. Supplied case 6: resource allowance=180; required resources=165, using the explicitly named quantities. Allowance margin equals maximum allowed resource units minus units required. What result follows?345 resource units16 resource units14 resource units15 resource units

15. A design meets three preferences but fails the mandatory load criterion in one listed case. What is warranted? Which inference or prediction is best supported?One failed case must be deleted.Missing load capacity is only a style choice.Revise or reject it for that case before selecting by preferences.Three preferences outvote the mandatory criterion.

16. Added reinforcement passes load but exceeds the material cap. What next? Which inference or prediction is best supported?Meeting one requirement proves combined feasibility.Recheck and satisfy both essential constraints together.Load success erases the budget failure.Delete the new material record.

17. A paper model must retain 20 L using at most four supplied panels. What makes a testable design question? Which inference or prediction is best supported?Ask only whether it is amazing.Ignore the cap once a preference score is high.Treat a drawing as verified physical deployment.Specify retention and panel-cap criteria, then compare supplied model results.

18. Supplied case 6: baseline loss (model units)=35; revised loss (same units)=23, using the explicitly named quantities. Loss reduction equals baseline loss minus revised loss. What result follows?13 model loss units11 model loss units12 model loss units58 model loss units

19. A second trial changes arrangement and input volume together. What limits the conclusion? Which inference or prediction is best supported?Confounding can be repaired by confident wording.Loss differences cannot be attributed uniquely to arrangement.More changed inputs always isolate causation better.Input volume cannot affect loss in any model.

20. A model predicts lower loss but no physical trial exists. What follow-up and claim fit? Which inference or prediction is best supported?State a conditional model prediction and propose a matched loss measurement; physical performance remains unverified.Declare real-world effectiveness already certified.Call missing observation a measured failure.Add invented physical results to close the gap.

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