The promise of digital herbarium specimens in large‐scale phenology research
Bibliographic record
Abstract
The online mobilization of herbaria has made tens of millions of specimens digitally available, revolutionizing investigations of phenology and plant responses to climate change. We identify two main themes associated with this growing body of research and highlight a selection of recent publications exemplifying: investigating phenology at large spatial and temporal scales and in understudied locations and testing long-standing theories and novel questions in ecology and evolution that were not previously answerable. We explore strengths and limitations of using digitized herbarium specimens in phenology research, including: issues of sampling; reliability, transferability, and biases; and ethical and social justice considerations. This field will see further breakthroughs as herbaria around the world continue to mobilize and digitally interlink their collections. New developments will likely come from advances in technology, international collaborations, and including understudied plant taxa and regions such as the Arctic and the tropics. Advances in technology are already improving digitization workflows and speeding the collection of phenology data from digital specimens.
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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 | 0.059 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".