Summary of Comments on Analysis of Hungarian red deer trophies by means of Principal component analysis in two different counties
Bibliographic record
Abstract
Authors analyzed data of 9 trophy parameters (weight of the antler, length of main beam, length of brow tine, length of bay tine, length of tray tine, circumference of coronet, lower circumference of main beam, upper circumference of main beam, number of total tines) of 6868 red deer stags shot between 1997 and 2007 and estimated ages were between 4-16 years, from two counties of Hungary (5946 from Somogy and 921 Bács-Kiskun). General linear model was used to evaluate age and “county” effects on the trophy parameters. Age was a significant source of variation for all studied traits while county affected most of the studied parameters. Consequently the dataset was analyzed separately for each county. Low to high correlations (adjusted for age effect) were found both in Somogy (r=-0.04 - 0.80) and for Bács-Kiskun (r=-0.06 - 0.70). Using principal component analysis (with orthogonal rotation) 4 factors were extracted which accounted for 73 % and 75 % of total variance in Bács-Kiskun and Somogy county respectively. The first factor represents the circumferences of the trophy, the second factor the main tines (brow, bay, tray) of the antler. The third and fourth factors represented the number of total tines of the trophies and the length of main beam respectively. These identified factors could be considered in selection/evaluation of the trophies in Hungarian red deer instead of the traditionally used measurements in order to maintain type and quality of the red deer trophy in Hungary.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".