Replication data for: Redefining dominance calculation: Increased competition flattens the dominance hierarchy in dairy cows
Bibliographic record
Abstract
Dominance hierarchies are known for mitigating conflicts and guiding priority of access to limited resources in gregarious animals. The dominance hierarchy of dairy cows is typically constructed using agonistic interactions, usually monitored at the feed bunk right after fresh feed delivery when competition is high resulting in numerous interactions. Yet, the outcome of agonistic interactions under time of high competition time may be more influenced by cows’ high valuation of fresh feed than their intrinsic dominance attributes. Thus, the dominance hierarchy constructed using agonistic interactions under high versus low competition times might differ. The aim of this study was to test how the structure of the dominance hierarchy changes in relation to different levels of competition. We monitored a dynamically changing group of 48 lactating dairy cows over 10 mo with 6 cows exchanged every 16 d, totally 159 cows. We used a validated algorithm to continuously detect the actor and reactor of replacement behaviors as cows competed for feed. We calculated feeder occupancy, the percentage of occupied feed bins, to characterize competition at the moment of each replacement, and created 25 corresponding dominance hierarchies using Elo ratings for occupancy levels ranging from 13% to 100%. With each 10% rise in feeder occupancy, hierarchy steepness fell by 0.02 (R2 = 0.96) and two-way dyads rose by 1.3% (R2 = 0.84). The win rate of the dominant cow within dyads declined with increased feeder occupancy (y = -0.11x -0.21, P < 0.001). Our findings provide evidence that there is noticeable variation in inferred hierarchies based on the competition context, with high competition flattening the hierarchy as subordinate animals succeed more in replacing others in order to gain feed access. This finding underscores that during heightened competition, the valuation of resources impacts agonistic behaviors and the subsequently constructed dominance hierarchy more than the individual's intrinsic dominance attributes. We recommend that researchers avoid using agonistic interactions occurring immediately after fresh feed delivery to establish dominance hierarchies. We also urge researchers to differentiate agonistic interactions based on context when constructing dominance hierarchies to draw inferences on animal behavior, cognition and health.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.360 | 0.167 |
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".