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Record W2914566105 · doi:10.4236/ce.2019.102021

Incorporation of High-Altitude Balloon Experiment in High School Science Classrooms

2019· article· en· W2914566105 on OpenAlexafffund
Stephen C. Cheng, David Gerhard, Fidji Gendron, Vincent E. Ziffle

Bibliographic record

VenueCreative Education · 2019
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsFirst Nations University of CanadaUniversity of Regina
FundersUniversity of Regina
KeywordsEffects of high altitude on humansThermochemistryMathematics educationAltitude (triangle)BalloonPhysicsEngineering physicsEnvironmental scienceMeteorologyAtmospheric sciencesPsychologyMathematicsMedicine

Abstract

fetched live from OpenAlex

We have been launching high-altitude balloons to engage students in science since 2013. Our custom balloon kit allows high school teachers and students to collect environmental data and capture videos. Through our experience interacting with high school students, we have found that the high-altitude balloon experiment is an effective tool for inquiry-based learning to introduce chemistry topics including gas properties, elements and molecules, heat capacity, thermochemistry, electromagnetic radiation, bond breaking and formation, and atmospheric chemical reactions. Examples are given to demonstrate how to incorporate the experiment in high school science classrooms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.233
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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