Detectability of species of <i>Carex</i> varies with abundance, morphology, and site complexity
Bibliographic record
Abstract
Abstract Questions Are graminoids more poorly detected than other life forms of vascular plants in surveys? How well do observer‐, species‐, and site‐specific variables explain variation in detection of Carex species across forests of different structure? Location Northeastern Alberta, Canada. Methods Species inventories were assessed within 50 belt transects, each 100 m in length and 2 m in width. Pseudoturnover was estimated for four life forms and all encountered species. Site‐specific factors were then compared with pseudoturnover of all vascular plants and graminoids using generalized linear regression. Carex detection probabilities were compared based on morphological groups. Detection success at a site and delays in detection within a site were assessed using logistic regression with AIC used to rank a‐priori hypotheses and standardized variables used to determine effect sizes of parameters related to plant detectability. Results Pseudoturnover for graminoids was similar to that for other life forms and best related to ground layer cover. Morphological groups related to differences in detection, with short, small‐inflorescence Carex most poorly detected. Detection failure was best explained by species abundance and morphology, but delays were more tied to a site's vegetation structure and species abundance than to species morphology. Conclusions Surveys targeting graminoids, including species of Carex, can achieve high detection rates with high survey effort over small areas, but should consider species‐ and site‐specific biases in detection success. Abundance is likely the most influential factor in determining detection success, and this must be accounted for when searching for low‐density species. We recommend that increased effort (time, repeat observations) be applied when searching for morphologically small graminoids.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".