Data Standards for the Phenology of Plant Specimens
Bibliographic record
Abstract
Phenological data (i.e., data on growth and reproductive events of organisms) are increasingly being used to study the effects of climate change, and biodiversity specimens have arisen as important sources of phenological data. However, phenological data are not expressly treated by the Darwin Core standard (Wieczorek et al. 2012), and specimen-based phenological data have been codified and stored in various Darwin Core fields using different vocabularies, making phenological data difficult to access, aggregate, and therefore analyze at scale across data sources. The California Phenology Network, an herbarium digitization collaboration launched in 2018, has harvested phenological data from over 1.4 million angiosperm specimens from California herbaria (Yost et al. 2020). We developed interim standards by which to score and store these data, but further development is needed for adoption of ideal phenological data standards into the Darwin Core. To this end, we are forming a Plant Specimen Phenology Task Group to develop a phenology extension for the Darwin Core standard. We will create fields into which phenological data can be entered and recommend a standardized vocabulary for use in these fields using the Plant Phenology Ontology (Stucky et al. 2018, Brenskelle et al. 2019). We invite all interested parties to become part of this Task Group and thereby contribute to the accesibility and use of these valuable data. In this talk, we will describe the need for plant phenological data standards, current challenges to developing such standards, and outline the next steps of the Task Group toward providing this valuable resource to the data user community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".