A Larval “Recruitment Kernel” to Predict Hatching Locations and Quantify Recruitment Patterns
Bibliographic record
Abstract
Abstract Larval recruitment, a critical component of population connectivity, has been under investigated compared to larval dispersal. We developed a backward‐in‐time Lagrangian particle tracking model to predict larval hatching locations and proposed a larval recruitment kernel, to quantify recruitment patterns. Combining field data and a hydrodynamic model, our backtracking model predicted Lake Whitefish (Coregonus clupeaformis) hatching locations in Lake Erie. We found a strong linear correlation (r = 0.95–0.98) between travel distance (i.e., distance along a trajectory) and pelagic larval duration (PLD), and a moderate correlation (r = 0.66–0.68) between linear distance (i.e., displacement) and PLD. This questions the wide use of PLD as a proxy for dispersal distance. We defined the recruitment kernel using the probability density function of the linear recruitment distance. Characteristics of the recruitment kernel, such as theoretical self‐recruitment, median‐recruitment distance, long‐distance recruitment, and openness convey significant information about population connectivity that are distinct from those derived using the well‐known dispersal kernel (e.g., theoretical local retention).
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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) | 0.000 | 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 | 0.001 |
| 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".