ANÁLISIS DE VACÍOS DE REPRESENTATIVIDAD EN LAS ÁREAS MARINAS PROTEGIDAS DEL SISTEMA DE PARQUES NACIONALES NATURALES DE COLOMBIA
Bibliographic record
Abstract
Gap representativeness analysis is the process that identifies and assesses biodiversity or a selected surrogate within a protected area system, determining which biodiversity elements are insufficiently represented or which areas are lacking protection. The main objective of this research was to perform a gap analysis based on physiographic areas recognized as coastal and ocean systems, as well as on biodiversity elements of intertidal and subtidal ecological systems with reference to 13 protected areas of the national system of natural parks of Colombia (SPNN) located at the Caribbean and Pacific marine and coastal zones. The spatial variability coverage of the 26 biodiversity elements from the Caribbean and 12 elements from the Pacific was assessed on the 18 coastal and oceanic systems that subdivide the study area. Through the use of the software ArcGis 9.2.1 the biodiversity elements were structured in a geographical information system enabling spatial explicit operations between the layers of marine protected boundaries, coastal systems and distribution of biodiversity surrogates. Based on the representativeness ranges defined in this analysis it was found that only three systems are properly represented in the SPNN (> 30%), four systems are under-represented (10 to 29%), four systems are represented less than 10%, and seven systems are not represented at all. In relation with the elements of biodiversity it was found that twenty elements are not represented ( 60%). According to the results, in order to increase the representativeness of biodiversity elements and to strengthen the SPNN it is recommended to undertake actions to achieve in situ conservation including the spatial variability along the marine and coastal areas of Colombia.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".