Spatial modeling of habitat trees based on line transect sampling and point pattern reconstruction
Bibliographic record
Abstract
An approach is presented for the spatial modeling of rare habitat trees surveyed by line transect sampling (LTS) in a protected area of the European Natura 2000 network. The observed tree pattern is defined as a realization of a thinned point process where the thinning can be modeled by a parametric detection function. A complete pattern is reconstructed using an optimization algorithm. The start configuration contains detected tree locations and randomly generated tree positions. Empirical cumulative distribution functions (ECDFs) for intertree and location-to-tree distances estimated from the original LTS are set as target characteristics. The same ECDFs are estimated by means of virtual LTS in the reconstruction. Tree positions are relocated during the optimization. The sum of squared deviations between the ECDFs from the original LTS and the virtual LTS in the reconstruction is considered as a contrast measure. A new configuration is accepted if the contrast is lowered compared with the previous state. The nonparametrically reconstructed habitat tree patterns are described by a log Gaussian Cox process model. Evaluations by means of line transect resamplings in a complete habitat pattern show small deviations between the second-order functional characteristics obtained from the true pattern and their analogs derived from the reconstructions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".