On Classifying the Skull Dimensions of the Wolf by the Discriminant Function
Bibliographic record
Abstract
This study examines the discriminant function analysis on the skull dimensions of samples of wolf skulls from northwestern Canada in four regions which include Rocky mountain males and Rocky mountain females as well as Arctic males and Arctic females. The variables that were measured in millimeters for each skull of a wolf are Y1: palatal length, Y2: postpalatal length, Y3: Zygomatic width, Y4: palatal width outside the first upper molar, Y5: palatal width inside the second upper premolars, Y6: width between the postglenoid foramina, Y7: interorbital width, Y8: least width of the braincase and Y9: crown length of the first upper molar. We produced the discriminant function equations for the four regions and stated the rules for classifying a certain variable that depicts a skull into one of the four regions considered in the study, that is, Rocky mountain males, Rocky mountain females, Arctic males and Arctic females. In this article, we employed the classification rules to classify each of the N = 25 statement vectors such that the classification and discrimination procedure asserted that 92.0% of the original grouped cases were correctly classified and 88.0% of the cross-validated grouped cases were correctly classified. The analyses in this article were analyzed and executed with the Statistical Package for Social Sciences (SPSS) software version 8.0
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".