Reducing the experimental error in an experiment to determine the latent heat of vaporization of liquid nitrogen
Bibliographic record
Abstract
In this work, modifications are made to a fairly simple laboratory experiment to experimentally determine the latent heat of vaporization with a substantially lower error than those previously reported. A new experimental technique of using an asynchronous data capture method is proposed for use with an adiabatic calorimeter for measurement as it isolates the measurement vessel from changes in the surroundings affecting the measured quantity. Further modifications are made to the experiment to determine the power at each instant the rate of mass drop is measured within the measurement range to ensure the errors associated with varying power resulting from resistance fluctuations are also minimized. The asynchronous data capture method proposes the use of computer control for the entire experiment, which again reduces the error in terms of delays in human reaction time. Applying these three new techniques to the experiment enables the latent heat of [Formula: see text] to be determined experimentally and results in a substantially lower error bar attached to the final value for the latent heat of vaporization of liquid nitrogen. The value of L v = 201.2 ± 0.194 J/g differs by approximately 1% from the accepted value of 199.0 J/g (G.W.C. Kaye and T.H. Laby. Tables of physical and chemical constants, fifteenth edition. Longman, London. 1995). Sources of systematic (experimental) error are suggested and estimated for in this experiment and thus this small discrepancy is accounted for.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".