How to find the rare trees in the forest — New inventory strategies for culturally modified trees in boreal Sweden
Bibliographic record
Abstract
Culturally modified trees (CMTs) in northern forests are rare traces of past human activity that provide unique information on past land use and the relationship between people and forests throughout history. There is an apparent need to provide probability sampling methods for these traces. This article describes the simulation and evaluation of circular plot sampling and strip surveying for estimating the density of culturally modified trees in 25 ha of a forest reserve in northern Sweden. CMTs were surveyed, documented, and prepared for use in simulator software and the bias, precision, and cost of different inventory strategies were calculated. For a given level of precision circular plot sampling was found to be more efficient than strip surveying for estimating the abundance frequencies of all CMTs. For smaller subpopulations of scarce CMT types, the strip-surveying method was superior. Probability sampling would be an important tool for examining larger areas and gaining more CMT information at a lower cost. The results are important for studies of cultural history in sparsely populated forested regions in northwestern North America, northern Scandinavia, and northern Russia, but there are also implications for finding other rare objects in forest ecosystems.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".