Analysis of the factors related with mate choice and reproductive success in male three‐spined sticklebacks
Bibliographic record
Abstract
Territorial three‐spined sticklebacks moved 5·3 times as far as non‐territorial males in 2 min (P< 0·001) and spent 11·1 times longer in aggression in the pools (P< 0·001). Territorial males had slightly higher condition factors than non‐territorial males. Condition factor was correlated positively with the gonad mass (P< 0·006), carotenoid concentration (P< 0·006) and the activity of CS in the axial muscle (P< 0·05) and lactate dehydrogenase (LDH) in pectoral muscle (P< 0·003). The male traits best correlated positively with female mate choice were courtship effort (P< 0·001), coloration (P< 0·003) and initial condition (P< 0·025). Courtship behaviour was related to intestine mass (P< 0·018), axial (P< 0·028) and pectoral muscle citrate synthase (CS) activity (P< 0·047); coloration was related to gonad mass (P< 0·037). These muscle enzymes may be involved in ATP generation for sustained activities or in recuperation between bouts of burst activity. Females that choose to mate with assiduously courting males which bear higher CS levels may be choosing individuals that show honestly their good condition and capacity to accomplish reproductive tasks.
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.
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) | 0.000 | 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) | 0.002 | 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".