Double sampling may improve the efficiency of litterfall estimatesContribution No. 2335 of the New Hampshire Agricultural Experiment Station, Durham, N.H.
Bibliographic record
Abstract
The effort required for an extensive litterfall measurement campaign can be prohibitive. We propose a double sampling approach, in which a large set of traps is used in each stand to estimate total litterfall, and only a subset of these traps is sorted to the relevant components. We examine its feasibility using data from a regional litterfall study of eastern white pine ( Pinus strobus L.), in which the variables of interest were biomass of foliar litterfall from pine and nitrogen content of foliar litterfall from all vegetation. Double sampling was more efficient than simple random sampling but only if every trap received a rapid presorting to remove twigs and cones. The optimal strategy when pine foliar litterfall biomass was the target variable was to conduct full sorting on 33% of the traps. When foliar litterfall N was the target, sorting only 20% of the traps was optimal. Holding time costs constant, the variance of estimated pine foliar litterfall biomass could be reduced by 18%, whereas that for foliar litterfall N could be reduced by 49%. Alternately, when variance was held constant, the time cost could be reduced by 17% for pine foliar litterfall biomass or 44% for foliar litterfall N.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".