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Record W1984065530 · doi:10.1007/s11270-009-0300-9

Mathematics: The Basis for Quantitative Knowledge

2009· article· en· W1984065530 on OpenAlexaff
J. T. Trevors, Milton H. Saier

Bibliographic record

VenueWater Air & Soil Pollution · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBasis (linear algebra)MathematicsMathematics educationComputer scienceManagement scienceEngineeringGeometry

Abstract

fetched live from OpenAlex

In this editorial, we consider the inference that mathematics has underpinned virtually all of science and engineering in the past and will become increasingly important in future research. Mathematics can be considered the foundation upon which humans can build an immense knowledge set to be used wisely for the good of humanity. In the pioneering days of science and technology, precise physical constants were discovered; explosive gun powder was prepared from the correct proportions of ingredients; temperature scales were designed; lenses for eye glasses, telescopes, and microscopes were precisely constructed; and the shapes and sizes of living organisms, cells, tissues, and organs were determined. Math was used to understand the rates of biochemical reactions, inhibitor effects, and the therapeutic doses of prescription drugs. We know the sizes of the elements in the periodic table and the charges and sizes of elemental particles making up matter. We know the estimated masses of planetary bodies and some galaxies, the speed of light, how to smash particles, and the ages of fossils. We use math and more maths in every aspect of our daily lives. Check, for example, the use of math on a cereal box. The evidence leading up to recognition of DNA as the genetic material of all cells required X-ray diffraction data, a knowledge of the numbers of hydrogen bonds between base pairs, and the math-dependent construction of models to solve DNA structural conformations. These advances, in turn provided an explanation for the precise mechanisms for replication of genetic material and transcription and translation of genes into mRNA and proteins. Math is everywhere. It comprises a language of its own that we have integrated into all our other languages. It allows us to make rational decisions and function on a day-to-day basis. We keep records and accounts to manage our routine and extraordinary activities. We can know what time it is in any time zone on Earth because of math. We can even estimate how much money one can save over a 10-year period by eating as a vegetarian rather than an omnivore, or what each of our contributions to global warming is, depending on our lifestyles. We use math to make predictions about the future. In fact, we have tremendous predictive powers. In recent years, math and mathematical modeling have become important approaches to understanding some of humanity's most pressing problems. In fact, the use of math in predicting human population growth and understanding the crisis of global climate change are literally in the news daily. We can even estimate when the Earth will become too hot for human existence. The use of math via computer systems is now used for making short- and long-term weather predictions, tracking hurricanes and tornadoes, estimating world food reserves and agricultural productivity, the speed of pandemic spread, the percentages of infected individuals, and the numbers of resultant deaths. Mathematics is central to understanding interconnected problems facing all of humanity. These problems, for example, concern the effects of human population growth on resource depletion and subsequent pollution of our biosphere. Without math, relevant calculations would be impossible. We have projections of human population growth, resource depletion, the numbers of humans without the basic necessities of life, and death rates from flooding and extreme weather conditions. We have reasonably reliable methods to calculate rates of polar ice melting, increases in average global temperatures, rates of consumption of our limited energy sources, and consequent rates of build-up of atmospheric greenhouse gases, and the consequences of rising sea levels. We can estimate (with accuracy decreasing with increasing time into the future) the times when coastal cities will be flooded and human populations must migrate. So far, unfortunately, most early estimates have proven to be too conservative, and each revision brings with it recognition that we have less time to act than had previously been claimed. Some of these statistics are not pleasant to think about, and even more difficult to deal with. However, without the use of math, humans would have no way to recognize the correct solutions. There is one interconnected problem that is causative of all the others: human population growth. The math is straightforward. Too many humans producing too much pollution equates to continued disaster. Every human on the planet can understand this essential precept, but too many politicians and corporate executives strive only to make money, even at the expense of humankind. Short-sightedness allows them to do so. We already have too many people and too much total global pollution. The prospect of sustainability may already be past the tipping point.

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)0.001
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 score0.130
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.304
Teacher spread0.280 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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