Training data and ensemble results of an ensemble of models trained to find flowers on herbarium sheet images
Bibliographic record
Abstract
This contains the 3 image datasets (training, validation, testing/holdout) for training models to identify flowers on images of herbarium sheets. Metadata for the images are located at https://github.com/rafelafrance/phenobase/blob/v1.0.0/datasets/splits_2025-04-22.csv. The 3 models were ultimately used in an ensemble for flower identification. flower_inference_formatted_2025-10-30.zip contains the formatted output of the ensemble and flower_inference_votes_2025-09-02.zip contains the raw ensemble votes. See the paper Petal to the metal? The slow road to automating large-scale phenology labeling for herbarium specimens Erin L. Grady1, Raphael LaFrance1, Daijiang Li2, Russell Dinnage3,4, Ellen G. Denny5, John Deck6 and Robert P. Guralnick1 1 Florida Museum of Natural History, University of Florida, Gainesville, Florida, USA 2 Department of Botany, University of Wisconsin-Madison, Madison, WI, USA 3 Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada 4 Alberta Machine Intelligence Institute, Edmonton, AB, Canada 5 USA National Phenology Network, School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA 6 Berkeley Natural History Museums, University of California, Berkeley, California, USA Corresponding author: Erin Grady; gradyerin@ufl.edu
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".