The Effect of Hurricane Iris on the Food Supply of Black Howlers (<i>Alouatta pigra</i>) in Southern Belize<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT Hurricanes frequently affect the forests of South and Central America; however, few studies have quantified their effects to forest structure, especially when concentrating on the food supply of an animal population. Hurricane Iris made landfall in Southern Belize on 8 October 2001, severely damaging a 52 hectare site where the behavioral ecology of a population of Central American Black Howlers (Alouatta pigra) had been under study for 2.5 yr. The hurricane resulted in a mortality rate of 35 percent for major food trees, which was primarily attributed to uprooting, snapping, and major delimbing. This damage accounted for 97 percent of the food tree loss between the two sample periods. Tree species differences were found in both the percentage loss and category of damage to food trees. Trees of different heights also experienced different percentage loss and levels of damage; subcanopy and emergent trees experienced higher loss than canopy trees, and subcanopy trees were frequently uprooted. This was partially attributed to a lack of buttressing on these subcanopy trees. Buttressing was found to decrease the frequency of uprooting. Tree size was the only factor that did not influence either damage or death. Trees from which fruit were eaten by black howlers died more than twice as often as did trees eaten for leaves.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".