Global scombrid life history data set
Bibliographic record
Abstract
Abstract The scombrids (Family Scombridae), commonly known as tunas, bonitos, Spanish mackerels, and mackerels, play an important role as predators and prey in coastal and oceanic marine ecosystems, and sustain some of the most important fisheries in the world. Knowledge of their basic biology and life history traits, such as growth, age, and maturity, is fundamental to sustainably manage these species, and maintain their critical role in marine ecosystems. Given the economic and social importance of their fisheries in many regions throughout the world, numerous life history studies have been conducted in the last century. Despite efforts to create global repositories of life history parameters, e.g., FishBase, many life history studies remain scattered and not readily accessible. Here, we compiled 667 life history studies published between 1933 and 2012 describing the growth, age, and reproductive biology of the 51 species of scombrids distributed around the world and create a standardized life history data set including maximum size, longevity, growth, maturity, fecundity, spawning season and frequency, and egg size information. We created this data set to promote the best use of the existing life history information and with the intention of providing a data resource suitable to test large‐scale ecological hypotheses on life history strategies and life history evolution, as well as support the management and conservation of this important group of commercially exploited species. We envisage the large repository of standardized life history data compiled will make this endeavor more effective and robust by providing a valuable resource that can help address many research questions.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".