Rural Inhabitant Perceptions of Sandhill Cranes in Northern Mexico Wintering Areas
Bibliographic record
Abstract
While a large proportion of the sandhill crane (Grus canadensis) population winters in northern Mexico, little information is available regarding conservation status of wetlands and human dimension issues. We conducted preliminary interviews of rural inhabitants living near wetlands used by cranes in 3 Mexican estates. One hundred percent of interviewees affirmed to know cranes, see them regularly (100%), and were capable of describing cranes. Winter is the time most have seen cranes (78%) with fall being second (20%). Most cranes were observed in lakes (56%), agriculture fields (35%), and cattle troughs (2%). Most responded to have seen 0-100 cranes (41%), while larger numbers were reported by smaller percentages. Most interviewees believed cranes eat corn (66%), oats (21%), sorghum (5%), and others items including wheat, insects, and cow droppings (2% each). Foraging was observed in agriculture fields (83%) with less in lakes (15%). Most did not know where cranes came from (71%), while smaller percentages said Canada (24%) and the United States (2%). A majority (58%) said they were not affected by the arrival of cranes, but 43% said they were. The negative effects were described as destroyed crops (31%), eating corn (23%), and diminished production. Those affected said they could implement scare tactics (70%), while others suggested harvesting on time (5%), checking crops regularly (5%), and hunting as possible solutions. Most (90%) said they did not hunt the cranes, 5% mentioned they used to and 3% said they still hunt them. These results offer a glimpse of the attitudes of rural inhabitants in northern Mexico towards cranes.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".