Développement d'un système d'information géographique (SIG) pour l'unité de gestion du nord du Nouveau-Brunswick de Parcs Canada
Bibliographic record
Abstract
Data Management is an essential component of the decision making process in Ecosystem Management and for maintaining ecological integrity in Parks Canada. A Geographical Information System (GIS) is a useful management, planning and decision making tool. The development of such a system for the New Brunswick North Field Unit will help to store and facilitate access to data in order to be efficiently used. The methodology of development followed is from a compilation of Theriault (1996). This methodology is based on seven steps and the validation is based on common criteria used. The information is organised in three main sections: Monitoring Programs, Studies and Information Sources. The structure of the GIS is composed of an administrator browser and an user one. The information is accessible via the LAN (Local Area Network). This system is developed with Powerpoint and Access. It organises as a series of browsers. These browsers enable access to these following information for each issue: Description of the project, Methodology and Protocols, Databases, Maps and Pictures, Contacts, Web Sites and Analysis Results. Links are hierarchical and relational. This project is based on a need analysis and results are illustrated by a general browser and three prototypes. One prototype is developed for each types of information. Then, several protocols have been developed to help the system in standardizing information. By the end, the project has been validated by comparison of the results and the following criteria: pertinence, flexibility, security, performance, independence, cooperation, motivation, progression and documentation.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".