Patterns and biases in an Arctic herbarium specimen collection: Implications for phenological research
Bibliographic record
Abstract
PREMISE OF THE STUDY: Herbarium specimens are increasingly used in phenological studies. However, natural history collections can have biases that influence the analysis of phenological events. Arctic environments, where remoteness and cold climate govern collection logistics, may give rise to unique or pronounced biases. METHODS: We assessed the presence of biases in time, space, phenological events, collectors, taxonomy, and plant traits across Nunavut using herbarium specimens accessioned at the National Herbarium of Canada (CAN). RESULTS: We found periods of high and low collection that corresponded to societal and institutional events; greater collection density close to common points of air and sea access; and preferences to collect plants at the flowering phase and in peak flower, and to collect particular taxa, flower colours, growth forms, and plant heights. One-quarter of collectors contributed 90% of the collection. DISCUSSION: Collections influenced by temporal and spatial biases have the potential to misrepresent phenology across space and time, whereas those shaped by the interests of collectors or the tendency to favour particular phenological stages, taxa, and plant traits could give rise to imbalanced phenological comparisons. Underlying collection patterns may vary among regions and institutions. To guide phenological analyses, we recommend routine assessment of any herbarium data set prior to its use.
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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 | 0.066 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".