Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347
Bibliographic record
Abstract
Tompalski et al. (2018) propose “template matching” as a (required) intermediate step to use remote sensing-based predictions of forest attributes as inputs of the Growth and Yield Projection System (GYPSY) for the simulations of forest stand dynamics in Alberta, Canada. Yet, the feasibility of the approach can be criticized for many points that call for experimental verification. The approach cannot be fully replicated based on the description of the paper. Nevertheless, an experimental implementation with synthetic data indicates that the quality of the projections may vary considerably depending on parameter assumptions for the templates, and the projections may include discontinuities between the observed and projected forest attributes. The approach is poorly motivated given that the effects described above are largely avoidable, if the underlying GYPSY models are run without the template matching step. The R-codes used for the analyses are provided as supplementary data for an interested reader wishing to evaluate the conclusions made above. A semantic analysis indicates further problems with multi-date data on a wall-to-wall grid. The projections obtained by template matching should be exposed to criticism for their realism and benchmarked against other approaches prior to using template matching as proposed by Tompalski et al.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.027 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.017 |
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".