Integrating Advanced Location Analytics and Machine Learning in Environmental Studies: A Cross Disciplinary Approach
Bibliographic record
Abstract
The cross disciplinary inclusion of location analytics and remote sensing data in biological data sets has provided a rich catalyst in biology research towards a more comprehensive understanding of the data generated in the study of living systems. Through a lens focused on case examples, we provide two examples of research methods that integrate remotely sensed data coupled with machine learning in discovering relationships between location analytics and biological phenomena. Our case studies include spatial dynamics in bird songs throughout disparate breeding geographies across Canada and the Northern US, and orchid life cycles in the White Mountains of New Hampshire. We added Landsat, Lidar and Hyperspectral data to existing biological data sets to determine the impact of vegetation and ground characteristics on outcomes. Our results demonstrate the viability of adding location-based data to biological data sets in collaboration with the original researchers. We explain the methods used in both case studies and postulate that a continued exploration of interdisciplinary collaborations may prove beneficial in the biological fields where spatial data exists or can be collected.
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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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".