Use of Smartphone and GIS Technology for Sustainable Forestry in Eastern Ontario
Bibliographic record
Abstract
This study examined whether the current generation of consumer-grade digital mobile computing technology, so called smartphone technology, is usable to perform and improve field data collection in the context of sustainable forest management. An electronic data acquisition system, based on a handheld smartphone device and desktop geographic information system (GIS), was developed. A proprietary timber cruise application and commercial mapping software were used with the smartphone/desktop GIS to record and process forest stand and geospatial data. Usability testing was carried out to measure workflow efficiency and system performance of the smartphone GIS compared to traditional paper-based methods. The smartphone GIS successfully met performance objectives and significantly increased workflow efficiencies by improving data transfer and processing times over conventional paper methods; however, use of the mobile device resulted in greater data entry errors, increased data collection times, and led to more equipment malfunctions than use of paper recording methods together with a GPS and digital camera. Overall, the prototype electronic data acquisition system was not reliable as a stand-alone solution solely responsible for collecting cruise data, but was found to be well suited for ad-hoc mapping of forest features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".