Two-phase approaches to point and transect relascope sampling of downed logs
Bibliographic record
Abstract
Point relascope sampling and transect relascope sampling were recently proposed as methods for the inventory of downed coarse woody debris. By only counting logs with a relascope device, the total length squared (with point relascope sampling) or the total length (with transect relascope sampling) of downed logs in an area can be estimated. For estimates of other variables, such as volume, additional measurements on the sampled logs are required. In this article, two-phase approaches to the methods are presented that makes use of the estimates from fast counts of logs as auxiliary data. The presented approaches serve two purposes: (i) to improve the efficiency of the methods and (ii) to avoid the bias that is likely to occur if careful checks of whether or not doubtful logs should be counted are neglected. Each two-phase design was compared with a single-phase design in terms of the standard error obtained for a given inventory cost. The two-phase designs decreased the standard errors with ca. 1718% for points and 1015% for lines. Including subjective judgements as additional auxiliary variables further decreased the standard errors in the line case but not in the point case. In the former case, the improvement in comparison with the single-phase design was 1723%.
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 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".