Modified formulas for calculation of encephalization: quotient in dogs
Bibliographic record
Abstract
Abstract Objective Dogs are a breed of animals that play important roles in security service, companionship, hunting, guard, work and models of research for application in humans. Intelligence is the key factor to success in life, most especially for dogs that are used for security purposes at the airports, seaports, public places, houses, schools and farms. However, it has been reported that there is correlation between intelligence, body weight, height and craniometry in human. In view of this, literatures were searched on body weight, height and body surface areas of ten dogs with intent to determining their comparative level of intelligence using encephalization quotient. Results Findings revealed that dogs have relationship of brain allometry with human as proven by encephalization quotient $$\left( {{\text{EQ}}} \right)\, = \,{\text{Brain Mass}}/0.{14}\, \times \,{\text{Body weight}}^{{0.{528}}} ,{\text{ Brain Mass}}/0.{12}\, \times \,{\text{Body Weight}}^{{0.{66}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mfenced> <mml:mtext>EQ</mml:mtext> </mml:mfenced> <mml:mspace/> <mml:mo>=</mml:mo> <mml:mspace/> <mml:mrow> <mml:mtext>Brain Mass</mml:mtext> </mml:mrow> <mml:mo>/</mml:mo> <mml:mn>0.14</mml:mn> <mml:mspace/> <mml:mo>×</mml:mo> <mml:mspace/> <mml:msup> <mml:mrow> <mml:mtext>Body weight</mml:mtext> </mml:mrow> <mml:mrow> <mml:mn>0.528</mml:mn> </mml:mrow> </mml:msup> <mml:mo>,</mml:mo> <mml:mrow> <mml:mspace/> <mml:mtext>Brain Mass</mml:mtext> </mml:mrow> <mml:mo>/</mml:mo> <mml:mn>0.12</mml:mn> <mml:mspace/> <mml:mo>×</mml:mo> <mml:mspace/> <mml:msup> <mml:mrow> <mml:mtext>Body Weight</mml:mtext> </mml:mrow> <mml:mrow> <mml:mn>0.66</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> and Brain Mass (E) = kpβ, where p is the body weight; k = 0.14 and β = 0.528, respectively. Saganuwa’s formula yielded better results as compared with the other formulas. Dogs with body surface area (BSA), weight and height similar to that of human are the most intelligent. Doberman pinscher is the most intelligent followed by German shepherd, Labrador retriever, Golden retriever, respectively.
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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 | 0.002 |
| 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".