The Petawawa Research Forest: Establishment of a remote sensing supersite
Bibliographic record
Abstract
The pace of technological change in forest inventory and monitoring over the past 50 years has been remarkable, largely asa result of the increased availability of various forms of remotely sensed data. Benchmarking sites, with the requisite refer-ence and baseline data for evaluating the capacities of new technologies, algorithms, and approaches, can be extremely valu-able for sparking innovation, as well as for enabling transparent and scientifically sound assessments of technologies, newdata streams, and associated information outcomes. Herein we describe the establishment of a remote sensing supersite atthe Petawawa Research Forest (PRF) in southern Ontario, Canada, and summarize the open access datasets that have beencompiled and made available to the public. The PRF is approximately 10 000 ha in size and represents a complex assemblageof tree species and forest structures. More than 1900 data records, including multiple airborne laser scanning datasets andassociated derivatives (i.e., digital terrain model, canopy height model), airborne imagery, satellite remote sensing timeseries, and ground plot data, among others, have been made openly available for download from Canada’s National ForestInformation System. We identify issues and present opportunities associated with the establishment of a remote sensingsupersite at the PRF, as well as share some of the lessons learned to foster the establishment and open data sharing for othernational and international remote sensing supersites. The PRF supersite can be accessed from the following link: https://opendata.nfis.org/mapserver/PRF.html .
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".