The Elephant Welfare Initiative: a model for advancing evidence‐based zoo animal welfare monitoring, assessment and enhancement
Bibliographic record
Abstract
The Elephant Welfare Initiative (EWI) is an effort supported by a community of member zoos with the common goal of advancing evidence‐based elephant‐care practices that enhance welfare. The idea for the EWI came about following the completion of a large‐scale North American elephant welfare study, which demonstrated that daily practices, such as social management, enrichment and exercise, play a critical role in improving the welfare of elephants in zoos. In 2014, the Elephant Taxon Advisory Group of the Association of Zoos and Aquariums expressed an interest in building upon the results of this study to support the continued assessment of elephant programmes and implementation of enhanced management practices. The EWI is supported by a web‐based system of software tools and resources. In contrast to traditional record‐keeping systems, the EWI tools provide participants with real‐time analysis as well as zoo‐ and elephant‐level metrics for key welfare indicators and associated management practices. Members’ data are pooled to create opportunities for benchmarking, and to leverage the collective efforts of individual organizations to address elephant welfare challenges and generate the data necessary to identify evidence‐based strategies for enhanced outcomes. Future considerations include extending the EWI model to other species in managed settings, and to support transitional programmes for in situ elephant reintroduction efforts.
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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 | 0.259 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.010 | 0.030 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".