The sspm R package for spatially-explicit surplusproduction population models
Bibliographic record
Abstract
Statement of needPopulation models are important tools for making management decisions, especially in fisheries, where predictive methods like Surplus Production Models (SPMs) are widely used.Fisheries analysts and managers often lack user-friendly, flexible tools to implement and apply SPMs.In addition, SPMs are rarely spatially explicit and usually cannot account for relevant ecosystem drivers.Therefore, there is a need for tools that implement spatially explicit surplus production models (SSPMs).The Northern Shrimp stock in the Newfoundland and Labrador Shelves is an example of a stock in need of an SSPM that can integrate important spatially-structured ecosystem drivers. SummaryPopulation modelling is an exercise of interest within environmental sciences and adjacent fields.Early population models such as the logistic model assumed that while the abundance of a population might change over time, the conditions governing parameters affecting that rate of change, such as the maximum rate of growth or the carrying capacity of the population, stay constant over time (Gotelli, 2008).Modern population models increasingly acknowledge the non-stationary nature of wild populations and work to incorporate environmental fluctuations into dynamic models (Thorson et al., 2015(Thorson et al., , 2017)).Population models designed to answer applied resource management questions, such as fisheries assessment models, increasingly address how the dynamics of stocks vary across space and time.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.120 | 0.058 |
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".