Preface: Workshop “Smart Forests – Forest ecosystem assessment and monitoring using Remote Sensing, Artificial Intelligence, and Robotics”
Bibliographic record
Abstract
New advances in sensors, platforms, data sciences have paved the way of constant observation and monitoring of ecosystems such as forest with unpresented high resolutions in space, spectrum, and time. This Smart Forests workshop focuses on the assessment and monitoring of forest ecosystems using state-of-the-art Remote Sensing (RS), artificial intelligence (AI), and robotics. Highlighted in this workshop will be emerging topics on data acquisition, pre-processing, information extraction, and forest remote sensing applications to support improved understanding of forest ecosystems, efficient management of forest resources, and multi-scale approaches for forest assessment and monitoring. One of the aims of this workshop will be to encourage discussions on innovative robotic operation of sensors, platforms, and AI powered processing technologies, from the perspectives of practical applications. The workshop also encourages the benchmarking of various data sources and processing methods for extracting key forest metrics and attributes and modelling forest processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.098 | 0.058 |
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".