Bibliographic record
Abstract
Abstract Double sampling involves combining information from inexpensive rapid counts with intensive complete counts to provide an efficient population estimate. This technique is a valuable approach for calibrating population indices based on incomplete counts and improving the precision of monitoring studies. Data collected through a double-sampling protocol can be analyzed with either a ratio or a regression estimator. The ratio estimator is recommended when the relationship of the actual count to the rapid count is a straight line through origin, which may not be valid when surveying populations that have a small number of individuals per site and a low detection probability. In such situations, the regression estimator may be more appropriate. I investigated the properties of these two different estimators through a simulation study and tabulated sample sizes to control bias of the population average and standard error. Further, I used the results to evaluate when double sampling is a cost-effective design and how to design surveys that meet precision requirements. The design process is illustrated with the Spring Eastern Waterfowl Survey, which shows that double sampling is not always appropriate. Directives pour l’utilisation de l’échantillonnage double dans le suivi des populations d’oiseaux
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".