Bibliographic record
Abstract
Investment theory is founded on the premise that higher returns are generally associated with greater risk, and that portfolio diversification reduces risk. Here I examine parental investment decisions in birds from this perspective, using data from a model system, a 16‐year study of breeding red‐winged blackbirds Agelaius phoeniceus . Like many altricial birds, blackbirds structure their brood into core (first‐hatched) and marginal (later‐hatched) elements that differ in risk profile. I measured risk in two ways: as the coefficient of variation in growth and survival of core and marginal offspring from a given brood structure; and using financial beta derived from the capital asset pricing model of modern portfolio theory. Financial beta correlates changes in asset value with changes in the value of a broader market, defined here as individual reproductive success vs. population reproductive success. Both measures of risk increased with larger core (but not marginal) brood size; and variation in growth and survival was significantly greater during ecologically adverse conditions. Core offspring showed low beta values relative to marginal progeny. The most common brood structures in the population exhibited the highest beta values for both core and marginal offspring: many parent blackbirds embraced rather than avoided risk. But they did so prudently with an investment strategy that resembled a financial instrument, the call option. A call option is a contingent claim on the future value of the asset, and is exercised only if asset value increases beyond a point fixed in advance. Otherwise the option lapses and the investor loses only the initial option price. Parents created high risk marginal progeny that were forfeited during ecological adversity (the option lapses) but raised otherwise (the option called); at the same time parents maintained a constant investment and return in low risk core progeny that varied little with changes in brood size or ecological conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".