A 20 y Analysis of Weather and Management Effects on a Small White Lady's-slipper (Cypripedium candidum) Population in Manitoba
Bibliographic record
Abstract
The small white lady's-slipper, Cypripedium candidum, is a rare perennial orchid with a limited distribution in Canada, occurring as isolated populations in remnant tallgrass prairie in southern Manitoba and Ontario. The species is listed as endangered in both provinces and as threatened federally. Despite its status, information on how environmental conditions and land management affect population size and persistence of this species is limited. We used 20 y of monitoring data collected for a subset of the largest population in Canada to evaluate the response of small white lady's-slipper to land management and weather. Long-term monitoring suggests the population is in decline and may not persist under the current climate and management regime. Temperature appears to regulate vegetative growth and flowering proximately. Warm temperatures early in the spring, when shoots are emerging, appear favored, but high temperatures during anthesis appear detrimental, reducing both vegetative growth and flowering. In contrast, precipitation appears to have a lag effect on growth and flowering. However, snow depth was identified as a positive influence on vegetative growth, suggesting precipitation in early spring, when shoots are emerging, is also important for above-ground growth. Some grazing appears to benefit the species presumably by reducing competition and shading, but frequent grazing may increase the risk of direct damage to individuals from cattle consumption and trampling and does not provide sufficient time for individuals to recover following grazing events. Our findings add to the knowledge of orchid conservation and management, highlighting the importance of long-term monitoring in detecting population trends in species with erratic life cycles and fluctuating populations, such as the small white lady's-slipper.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".