Dormancy in Caladenia: a Bayesian approach to evaluating latency
Bibliographic record
Abstract
Dormancy is common in many terrestrial orchids in southern Australia and other temperate environments. The difficulty for conservation and management when considering dormancy is ascertaining whether non-emergent plants are dormant or dead. Here we use a multi-state capture–recapture method, undertaken over several seasons, to determine the likelihood of a plant becoming dormant or dying following its annual emergent period and evaluate the frequency of the length of dormancy. We assess the transition probabilities from time series of varying lengths for the following nine terrestrial orchids in the genus Caladenia: C. amoena, C. argocalla, C. clavigera, C. elegans, C. graniticola, C. macroclavia, C. oenochila, C. rosella and C. valida from Victoria, South Australia and Western Australia. We used a Bayesian approach for estimating survivorship, dormancy and the likelihood of death from capture–recapture data. Considering all species together, the probability of surviving from one year to the next was ~86%, whereas the likelihood of observing an individual above ground in two consecutive years was ~74%. All species showed dormancy of predominantly 1 year, whereas dormancy of three or more years was extremely rare (<2%). The results have practical implications for conservation, in that (1) population sizes of Caladenia species are more easily estimated by being able to distinguish the likelihood of an unseen individual being dormant or dead, (2) population dynamics of individuals can be evaluated by using a 1–3-year dormancy period and (3) survey effort is not wasted on monitoring individuals that have not emerged for many years.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".