Conservation-related knowledge, interactions, and attitudes of local people toward Grey Crowned-Cranes ( Balearica regulorum ) in Tanzania
Bibliographic record
Abstract
The endangered Grey Crowned-Crane (Balearica regulorum) occurs extensively in agricultural areas and grasslands outside of protected areas in Tanzania, posing high potential for conflict with people. This study sought to determine the extent of crop depredation by cranes, extent of illegal crane trade, and attitudes towards and interactions of local people with Grey Crowned-Cranes. We interviewed 570 respondents (44% female) from 42 rural communities across four districts in Tanzania. Most of the respondents were farmers (n = 288), followed by livestock keepers (n = 169), businesspersons (n = 75), government employees (n = 24), and others (n = 14). Overall, 91% of the respondents indicated that Grey Crowned-Cranes were not a pest to crops but, for those reporting damage, farmers with mixed or other types of crops (maize, beans, bananas, tomatoes) reported the highest frequency of damage. The respondents had positive interactions with cranes, with 96% responding that they caused no harm to the cranes and 4% saying they used trapping and chasing of cranes to control crop damage. There was evidence of crane trade, with 6% of the respondents reporting having seen illegal collection of cranes or taking crane eggs or chicks. The reported illegal collection of cranes occurred mainly in the Mbeya Region for use in traditional medicine. Overall, respondents had positive attitudes towards Grey Crowned-Cranes, and we recommend that crane conservation education programs be developed and delivered to rural communities to enhance Grey Crowned-Crane conservation in Tanzania.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".