INCREASED EGG CONSERVATION-IS IT ESSENTIAL FOR RECOVERY OF WHOOPINGCRANES IN THE ARANSASIWOOD BUFFALO POPULATION?
Bibliographic record
Abstract
The whooping crane (Grus americana) is in a race for survival against adversities (genetic, demographic, and environmental) that are only partially understood. There is increasing evidence of genetic problems (drift, inbreeding, and loss of heterozygosity) in the captive population that likely also exist in the wild Aransas-Wood Buffalo Population (A WP), a consequence of the 1940s population bottleneck. Small populations are vulnerable to extinction through catastrophic events and random changes in productivity or survival. Negative environmental effects faced by whooping cranes include upstream diversion which diminish freshwater (nutrient) inflow into Texas wintering habitats, and expanding human activities along the coast of the Gulf of Mexico. Population and genetic specialists tell us that security against genetic problems, demographic fluctuations, and environmental changes, lies in maximizing population size. An appropriate minimum population goal to overcome the aforementioned problems is 1,000 individuals (Shaffer 1981, Salwasser et al. 1984, Mirande et al. 1993). The Canadian- United States Whooping Crane Recovery Team has accepted 1,000 birds as their goal for the A WP. If habitat is not limiting and inbreeding does not depress viability (rather large uncertainties), another 30+ years must pass before the A WP reaches 1,000 individuals (Mirande et al. 1993). Can the A WP survive 30+ years to reach a minimum secure population level? It seems evident that managers should be cautious and consider what might be done to accelerate A WP growth. Two potential techniques come to mind. One would be to supplement the population with introductions of captive-reared cranes. In previous brief discussions by the recovery teams, this approach has been discounted because of potential disease transmission to the only wild self-sustaining population. The second technique would be to initiate intensive egg management (Fig. 1) as described by Ellis and Gee (2001).
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".