Exploratory Analysis of Biometric Data Concerning Characteristics of Urucum (Bixa orellana L.) in the Northeast of Brazil
Bibliographic record
Abstract
Urucum is a plant adapted to the soil and climate conditions of the semi-arid region. This study evaluates the biometry of urucum seeds. Twenty seeds of annatto were collected in an area of native vegetation with presence of the species, located in the Mossoró Mountains in the municipality of Mossoró, State of Rio Grande do Norte, Northeast Brazil in July 2017 and taken to the plant breeding laboratory, where the following characteristics were evaluated: (a) morphological characterization of the seed being determined the length and width in millimeters, of 200 seeds well developed, with the aid of a pachymeter with precision of 0.1mm and (b) weight of the seed expressed in grams. Descriptive and graphical analyzes were carried out using the statistical software R. The length and width showed a small range of variation, resulting in excellent and reasonable values of coefficients of variation, respectively. We found a regular degree of symmetry and a mesokurtic distribution for length and width of seeds. There was no significant linear correlation between length and width. The features of urucum seeds did not fit to the normal distribution of probability.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Biometric analysis of annatto seeds; agronomy.
This study measures plant seed characteristics and does not study research methods.
Biometric measurement of urucum seeds; agronomic domain study, not research methods as object.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".