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Record W7020714154

MAPSAR simulation campaign: evaluation of the SIVAM/SIPAM SAR system for geologic mapping in Carajás Mineral Province

2007· article· en· W7020714154 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2007
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsnot available
Fundersnot available
KeywordsGeologic mapMineral explorationLithologyMineralProspectingMineral resource classification
DOInot available

Abstract

fetched live from OpenAlex

The Carajás Mineral Province is located in the eastern portion of the Amazon craton, state of Pará and contains a significant number of mineral deposits, most of them exhibiting structural and lithological controls, such as sets of dilation faults. This paper presents the results of the interpretation of simulated MAPSAR (Multi-Application Purpose SAR) data, integrated with aerogeophysical data, over a portion of the Province. The integrated SAR-geophysical data were assessed as auxiliary tools for geological and structural mapping, using data fusion techniques for generating imagery for geological interpretation. SAR images were produced by the SIPAM R99-B system and the airborne geophysical data by the Brazil-Canada Geophysical Project. Prior to fusion, SAR and geophysical data were individually processed for enhancing geological information. The results allowed establishing the relationship between the features extracted from fused images and the main lithologic and geomorphologic domains known in the area. These results demonstrate the potential of these data and methods for the geological analysis of areas of high metallogenetic potential in the Amazon, in support to regional mineral exploration programs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.152
GPT teacher head0.402
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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Same venueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research)Same topicAdrenal and Paraganglionic TumorsFrench-language works237,207