A Web and Mobile System for Environmental Decision Support
Bibliographic record
Abstract
Current field data collection methods for many of today’s scientific and other observer/monitor type applications are still entrenched in the “clipboard age”, requiring manual data transcription to a database management system at some (often much) later date, and only allows for visualisation and analysis of recently captured field data “back in the lab”. This chapter is targeted at progressing today’s pen & paper methodology into the spatially enabled mobile computing age of realtime multi-media data input, integration, visualisation, and analysis simultaneously both in the field and the lab. The system described is customized to the specific needs of the Canadian Great Lakes Laboratory for Fisheries and Aquatic Sciences Fish Habitat Management Group requirements for fish species at risk assessment, but is ready for adaptation to other environmental agency applications (e.g. forestry, health-pesticide monitoring, agriculture, etc.). The chapter is ideally suited to all agencies responsible for collecting field data of any type that have not yet moved to a state-of-the-art mobile and wireless data collection, visualisation, and analysis work methodology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.121 | 0.106 |
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".