Recovery of the South American sea lion (<i>Otaria flavescens</i>) population in northern Patagonia
Bibliographic record
Abstract
The size of and trend in the South American sea lion (Otaria flavescens) population located in northern Patagonia were estimated and changes in the distribution, size, and structure of individual sites were analyzed during the period 19832002. Total counts were made during the reproductive season. Regression models were used to analyze the trend. Pups represented around 40% of the animals counted. The annual rates of change for pups and nonpups were not significantly different (p > 0.05, n = 7), although some rookeries showed higher rates of change for pups than for nonpups. Pup numbers have been increasing at the rate of 3.4% per year at the oldest rookeries, but the rate of increase was higher at new rookeries. Using Bayes' methods, the precision of the estimates and the contribution to the abundance of each rookery produced an alternative estimate of the trend in pup numbers in 5.7%. The key in the recovery of this population includes higher survival rates of juveniles combined with increased available habitat for newly reproducing individuals. This process led to the occupancy of new areas for hauling out and breeding. This hypothesis could explain the higher rates of increase in pups in peripheral areas while reproductive rates remain unchanged.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".