LEVERAGING THE WILDLIFE INSIGHTS PLATFORM TO BUILD AI LITERACY AND ANALYTICAL SKILLS IN FUTURE ENVIRONMENTAL SPECIALISTS
Bibliographic record
Abstract
The contemporary biodiversity crisis requires qualitatively new approaches to the training of environmental specialists who would be proficient with tools for the digital analysis of data from protected natural areas.The purpose of the study was to develop and evaluate the effectiveness of an educational approach based on the use of artificial intelligence tools to develop the competencies of environmental science students in the field of analyzing data from specially protected natural areas (SPNA).The pedagogical experiment involved 48 3rd-year students randomly assigned to the experimental (n = 24) and control (n = 24) groups.The experimental group underwent three-stage training to work with the Wildlife Insights platform to automatically identify animals in images from camera traps.The effectiveness of the approach was assessed by comparing the level of competency development, the quality of data analysis, and environmental thinking.The results showed a statistically significant superiority of the experimental group: the median total test score was 18.0 against 11.0 in the control group (U = 32.0,p < 0.001), the accuracy of species identification was 95.0% vs 78.0% (U = 89.5, p < 0.001), and task completion time was 2.9 times shorter.Students in the experimental group were more likely to identify complicated ecological patterns (87.5% vs 29.2%) and showed greater readiness to use digital tools in professional practice.The developed approach can be scaled for various environmental education programs, contributing to the goals of the Kunming-Montreal Framework for the effective management of protected natural areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".