Separating genetic and environmental influences on temporal spawning distributions of largemouth bass (<i>Micropterus salmoides</i>)
Bibliographic record
Abstract
Environmental and genetic factors influence fish spawning periodicity (i.e., the distribution of spawning events during the breeding season), but their relative contributions have rarely been evaluated. We evaluated the relative contribution of genetic and environmental effects on spawning periodicity by rearing Florida largemouth bass (FLMB, Micropterus salmoides floridanus) from Lake Okeechobee and intergrade largemouth bass (ILMB, Micropterus salmoides salmoides × M. s. floridanus) from Lake Seminole in a similar environment. Fish from each genetic source population were translocated to experimental ponds at an intermediate latitude in Gainesville, Florida, in September 2003. We used estimated ages of offspring as an index of spawning events to compare spawning distributions between brood sources in ponds and related those results to spawning distributions at source populations for 2004. FLMB began spawning earlier than ILMB in all ponds, and FLMB had a longer spawning season than ILMB. Similarly, FLMB at Lake Okeechobee began spawning earlier and had a longer spawning season than ILMB at Lake Seminole. Environmental factors (e.g., temperature effects) influenced spawning periodicity for both FLMB and ILMB, but spawning periodicity was also influenced by genetic composition in ponds because translocated fish reflected characteristics of their source populations. Thus, both environmental factors and genetic composition influenced spawning periodicity.
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 | 0.001 | 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) | 0.001 | 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".