National Spatial Data Clearinghouses: Worldwide development and impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
However, the national clearinghouse differs due to the fact that it is embedded in the National SDI.In April 2005, 83 national clearinghouses were established on the Internet.A few examples of current national clearinghouses are: MIDAS (MetaInformacni Databazovy System), Czech Republic; geodata-info.dk,Denmark; India NSDI Portal, India; Spatial Data Catalogue, Malawi; Russian GIS Resources, Russia; Geocat.ch,Switzerland; and the Clearinghouse Nacional de Datos Geograficos del Uruguay, Uruguay.Those listed, share the same objective, that of discovering and accessing spatial data, through the available metadata.National clearinghouses are evolving worldwide.These developments have contributed to the realisation of national SDIs.A body of literature has been compiled on national experiences (e.g.Spatial Applications Division, Catholic University of Leuven 2003, conference papers of Global Spatial Data Infrastructure Association 2002-2005).So far, the majority of this literature focuses on the technical aspects of clearinghouses, and does not take into account the evolutionary nature of these electronic facilities.It is important to have a longitudinal perspective when establishing and maintaining clearinghouses.A detailed study of developments of all national clearinghouses worldwide could be an appropriate starting point.This could identify the critical factors behind the success or failure of a clearinghouse.In this way, knowledge could be used for the support of future implementation strategies.Factors for consideration could be societal, for instance legal, economic, technological, historical, cultural, demographic, environmental and institutional characteristics of a country, or clearinghouse-internal, such as the network architecture, availability of view services, type of search mechanisms and funding stability.However it is worth noting that, simply consolidating the best practices of a few well-operating national spatial data clearinghouses (Australia, Canada and USA), gives no guarantee of sustainability for other national clearinghouses.Such best practices cannot necessarily be applied equally in other countries
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it