Case Study: A Split–brood Comparison of Formula for Nestling Songbirds (FoNS ) versus Three Facility–specific Diets
Bibliographic record
Abstract
Thirty–four nestling birds of seven species were used to test whether Formula for Nestling Songbirds (FoNS ) would result in better weight gain, feather growth, and survival rates compared to inexpensive control diets. Ten broods, of 2 to 5 birds, were split into two groups. Some of each brood received FoNS while others received one of three species specific and facility–formulated control diets. Four species (Bewick’s wren [Thryomanes bewickii], red–eyed vireo [Vireo olivaceus], violet–green swallow [Tachycineta thalassina], and black–capped chickadee [Poecile atricapillus]), received a control diet consisting of dog food supplemented with bone meal, brewer’s yeast, and protein powder. With the exception of the swallows, birds on this control diet generally lost weight, and three of the five died; whereas all six birds from the same broods survived and gained weight on FoNS . Two species, (house sparrow [Passer domesticus] and house finch [Carpodacus mexicanus]) received control diets of chick starter with cat food, hard boiled eggs, bone meal powder, and peanut butter, and one species (European starling [Sturnus vulgaris]) received a control diet based on dog food. For these species, the FoNS diet and control diets showed similar development, growth, and no deaths. The authors suggest that FoNS diet is advantageous, especially for small, insectivorous species, but some inexpensive alternatives may be equally suitable for certain species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".