Espectro de polen corbicular de Apis mellifera en muestras de Huejotitán, Jalisco, México
Bibliographic record
Abstract
This study examines the different plants visited by the honeybee (Apis mellifera L.) during the honey harvest season (August to November) 2012. The work consisted in identifying the corbicular pollen pellets collected by the bees in one apiary in the village of Huejotitan, municipality of Jocotepec, state of Jalisco, Mexico. Three hives were selected and sampled monthly by means of Ontario modified pollen traps. The samples were tagged and frozen and later processed by acetolysis technique to remove the exine; permanent glycerine slides were made for the preservation and analysis. Identification and counting of pollen grains was performed using an Olympus BH-2® upright microscope equipped with a 100X ocular micrometer to measure each individual species pollen grain, using immersion oil. Wild plants in bloom were also collected monthly, tagged, pressed and taken to the herbarium for identification; the pollen was extracted, processed and identified for a reference collection that served as an ancillary means of identification and as a seasonal reference to the blooming species. In the corbicular pollen, 23 types of plants were identified: 13 at species level, five at genus level and five at family level belonging to 17 plant families. Myrtaceae resulted the most frequently represented family followed by Asteraceae, Fabaceae and Lamiaceae.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".