An Islandscape IFD: Using the Ideal Free Distribution to Predict Pre-Columbian Settlements from Grenada to St. Vincent, Eastern Caribbean
Bibliographic record
Abstract
This study employs an ideal free distribution (IFD) model to conduct a fine-grained analysis of environmental factors affecting the pre-Columbian colonisation sequence and settlement patterning in the southern Lesser Antilles of the Eastern Caribbean. We compiled a database of all known archaeological site locations and associated chronological data from St. Vincent, the Grenadines, and Grenada, and vetted this dataset for accuracy. We then performed multivariate statistical analysis of the vetted site data and 24 environmental variables hypothesised to influence settlement habitat quality, including soil attributes, proximity to freshwater/stream beds, structure and sizes of marine environments, and net primary productivity (NPP) layers. Iterative testing and refinement of the model allowed for the creation of a predictive map of pre-Columbian archaeological sites over time. Results indicate proximity to freshwater wetlands, NPP, and reef size were important variables influencing habitat choice. Additionally, latitude (distance from the equator) was also a significant variable, indicating support for a proposed colonisation of the southern Lesser Antilles that began in the northern Caribbean, rather than the south. Lastly, we provide a site inventory and map of predicted site locations that can aid in the management of threatened archaeological resources within the study region.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".