TECHNIQUES FOR STUDYING INTEGRATED IMMUNE FUNCTION IN BIRDS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
EVOLUTIONARY PHYSIOLOGISTS AND ecologists seek to understand the mechanisms that underlie trade-offs involving life-history traits. These trade-offs arise when resources are limited, and allocation of resources to certain traits limits the amount of resources available for other traits (Williams 1966, Stearns 1992). Recent studies have found that maintaining or activating immune function can be resource-dependent (Tsiagbe et al. 1987; Saino et al. 1997b, 2003; Alonso-Alvarez and Tella 2001) and metabolically costly (Demas et al. 1997, Lochmiller and Deerenberg 2000, Ots et al. 2001, Martin et al. 2002) and, therefore, may compete with lifehistory traits for nutrient or energetic resources (for reviews see Sheldon and Verhulst 1996, Lochmiller and Deerenberg 2000). Therefore, the study of immunocompetence (i.e. the ability of a host to prevent or control infection by pathogens and parasites) has become the focus of many studies on fitness-related trade-offs in free-living birds (Saino et al. 1997a; Horak et al. 2000; Norris and Evans 2000; Hanssen et al. 2003, 2004). In their seminal review, Norris and Evans (2000) contrasted the techniques currently used by ecologists to measure immunocompetence with the techniques used by immunologists. Reviewing examples of trade-offs between immune-system maintenance and resource allocation to life-history traits, they concluded that future studies need to (1) assess multiple components of the immune system to make conclusions about how these components interact and
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it