Species richness estimation of bird communities: how to control for sampling effort?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since estimates of total species richness increase with sampling effort, methods to control for this sampling effect need to be tested and used. We present seven non‐parametric and 12 accumulation curve methods that have been used recently in the ecological literature. To test their performance, we used data from bird communities in the Queen Charlotte Islands, Canada. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. For our data set, the two Chao estimators were the overall least biased and most precise estimation methods, followed by the two jackknife estimators, thus supporting results of previous studies. Nonparametric estimators tended to perform better than accumulation curve models. Most estimation methods had the problem that they tended to underestimate species richness for early samples, but slightly overestimated it for late samples. We briefly discuss the practical use of these methods which may greatly increase our ability to answer ecological questions and to guide conservation decisions, especially for species‐rich tropical bird communities.
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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 | 0.000 |
| 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 it