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Record W4297997846 · doi:10.5539/jmr.v14n5p36

Extended Sine and Cosine Functions for Scalene Triangles

2022· article· en· W4297997846 on OpenAlexvenueno aff
Luis Teia

Bibliographic record

VenueJournal of Mathematics Research · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSineMathematicsTrigonometric functionsComputationSine and cosine transformsMathematical analysisCombinatoricsGeometryAlgorithmFourier transform

Abstract

fetched live from OpenAlex

This article pushes the role of sine and cosine functions beyond the traditional purpose of determining the sides of a right triangle, into the realm of determining the lengths of the sides of any triangle with practically the same ease. Extended functions are formulated dependent on two angles (instead of the traditional one) — sin*(α,γ) and cos*(α,γ)—that allow (via direct application) the computation of the lengths of the two shorter sides of a scalene triangle, as a result of the angular projection (from reference angle γ and a variable obtuse angle ) of the longer side or extended hypotenuse (for right triangles, the obtuse angle is fixed to γ= 90 deg, allowing only the variation of α— a significant limitation). When integrated into larger more complex mathematical formula, the extended sine and cosine functions add greater flexibility and open the door for the mathematician or scientist to explore possibilities that are non-orthogonal. Solved exercises are provided at the end, with the purpose of illustrating the robustness and advantage of the application of these new extended sine and cosine functions to determine the normalized sides of a scalene triangle—a requirement that is present virtually in any technical discipline.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.125
GPT teacher head0.380
Teacher spread0.255 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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