Mass or weight: What is measured and what should be reported—Response to Murray (2007)
Bibliographic record
Abstract
In his commentary on Chardine (1986), Murray (2007) advises ornithologists to use the term “weight” in preference to “mass.” His argument is largely based on the history of how mass was, and still is, measured. We weigh things to determine mass, so why not call the measurement “weight”? After all, “weight” is used to refer to mass in trade and commerce and in colloquial English. The problem with this argument is that in science, weight and mass are different properties with different associated units, and confusing the terms leads to ambiguity and loss of clarity. Mass is the amount of matter in an object and is expressed in SI units (see Acknowledgments) of kilograms. Weight is the force of gravity on an object and is expressed in SI units of newtons (in base SI units, 1 newton = 1 kg.m.s−2). Semantic confusion of mass and weight could lead to the use of incorrect units in calculations. Pennycuick (1987) presented a nice example of the fallacy of substituting mass units for weight units in animal flight calculations. The reverse is just as serious. For example, in calorimetric studies, the energy content of avian foods is usually determined by the controlled burning of a known mass in a bomb calorimeter. The energy density of the material can then be calculated by dividing energy released by the mass of material burned; this would be correctly reported in SI units of joule.kg−1. Substituting weight units instead of mass into the calculation of energy density creates a result with units of joule.newton−1, which in base SI units cancels to distance in meters! Murray recommends that we use the term “weight” instead of “mass” but use the “incorrect units, kilograms (or grams).” Surely this cannot be defensible in a modern science such as ornithology. When we mean “amount of matter,” we should use the term “mass,” expressed in kilogram units. When we mean “the force of gravity on an object,” we should use the term “weight,” expressed in newton units. I advocated this in so many words more than 20 years ago (Chardine 1986) and see no reason to change now, especially as ornithology becomes increasingly quantitative and mathematical. Acknowledgments.—I thank C. J. Pennycuick for valuable discussion of this subject and P. W Hicklin for comments on an earlier version of the manuscript. For SI units, see Bureau international des poids et mesures at www.bipm.org/en/si/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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 source (direct Gemma or distilled Codex), 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".