Bibliographic record
Abstract
From the oracles of Greek antiquity to the crystal balls of druids, the possibility of predicting future events has captured human imagination for millennia. Even modern scientists and wildlife managers are not immune to this desire. For decades, researchers have tried to develop reliable methods for predicting the survival of wild animals. In particular, scientists have attempted to connect mechanistic physiological measurements with subsequent survival. Frustratingly for researchers, most of the parameters measured to date are not reliable predictors of survival. It seems that many physiological parameters are too sensitive to current conditions and are therefore too changeable to be used to predict future survival. However, a recent paper by Lee Koren and colleagues presents a promising method for survival rate prediction. The worldwide team of researchers from Canada, Israel, New Zealand and the United Kingdom used novel hormone measurements to predict survival in wild house sparrows (Passer domesticus).Previous research has attempted to correlate hormone levels with subsequent survival, but with limited success. Koren and colleagues approached the problem differently.First, hormones in wild animals are typically measured in blood or faecal samples. Such samples reflect current hormone levels and tend to be highly dependent on current conditions, and are therefore highly variable. Instead, Koren and colleagues measured hormone levels in feathers grown after the autumn moult to obtain an incorporated measure of hormone levels during the period of feather growth.Second, hormones are typically measured using labelled antibodies that bind to a hormone of interest. But antibody binding is inherently inaccurate, as antibodies are never entirely specific to the hormone of interest. In the current study, Koren and colleagues avoided the errors associated with non-specific antibody binding and inaccurate sampling by measuring feather hormone levels with mass spectrometry. Using this novel approach, they showed that feather hormone levels predict the birds’ overwinter survival and that birds with lower feather hormone levels are more likely to survive the subsequent winter.The hormones measured in the study by Koren and colleagues were cortisol, corticosterone and testosterone. Cortisol and corticosterone are associated with the stress response, while testosterone is an important sex hormone. All three hormones are associated with energy use and metabolism in both male and female birds. The team suggests that differences in energetic needs or stored energy reserves during the autumn might be reflected in the feather hormone concentrations and may then influence overwinter survival. Interestingly, while corticosterone is a well-known stress hormone in birds, this paper is one of the first to demonstrate that birds also produce cortisol.The use of hormone measurements from feather samples to predict survival potentially offers a new tool for wildlife managers to predict the survival of wild animals. Such a tool is a valuable addition to population management and conservation. Forget tea leaves and tarot cards; at least for house sparrows, feather hormones predict the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".