MétaCan
Menu
Back to cohort
Record W2474542453 · doi:10.3390/rs8070579

Distribution of Artisanal and Small-Scale Gold Mining in the Tapajós River Basin (Brazilian Amazon) over the Past 40 Years and Relationship with Water Siltation

2016· article· en· W2474542453 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRemote Sensing · 2016
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Victoria
FundersInstituto Chico Mendes de Conservação da BiodiversidadeNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e TecnológicoCiência sem FronteirasFundação de Amparo à Pesquisa do Estado de São PauloUniversity of Victoria
KeywordsSiltationAmazon rainforestEnvironmental scienceScale (ratio)Remote sensingStructural basinSatellite imageryGold miningHydrology (agriculture)Physical geographyDatabaseWater resource managementGeographyGeologySedimentCartographyComputer scienceGeomorphology

Abstract

fetched live from OpenAlex

An innovative remote sensing approach that combines land-use change and water quality information is proposed in order to investigate if Artisanal and Small-scale Gold Mining (ASGM) area extension is associated with water siltation in the Tapajós River Basin (Brazil), containing the largest small-scale gold mining district in the world. Taking advantage of a 40-year period of the multi-satellite imagery archive, the objective of this paper is to build a normalized time-series in order to evaluate the influence of temporal mining expansion on the water siltation data (TSS, Total Suspended Solids concentration) derived from previous research. The methodological approach was set to deliver a full characterization of the ASGM expansion from its initial stages in the early 1970s to the present. First, based on IRS/LISSIII images acquired in 2012, the historical Landsat image database (1973–2001) was corrected for radiometric and atmospheric effects using dark vegetation as reference to create a normalized time-series. Next, a complete update of the mining areas distribution in 2012 derived from the TerraClass Project (an official land-use classification for the Brazilian Amazon) was conducted having IRS/LISSIII as the base map with the support of auxiliary data and vector editing. Once the ASGM in 2012 was quantified (261.7 km2) and validated with photos, a reverse classification of ASGM in 2001 (171.7 km2), 1993 (166.3 km2), 1984 (47.5 km2), and 1973 (15.4 km2) with the use of Landsat archives was applied. This procedure relies on the assumption that ASGM changes in the land cover are severe and remain detectable from satellite sensors for decades. The mining expansion area over time was then combined with the (TSS) data retrieved from the same atmospherically corrected satellite imagery based on the literature. In terms of gold mining expansion and water siltation effects, four main periods of ASGM activities were identified in the study area: (i) 1958–1977, first occurrence of mining activities and low water impacts; (ii) 1978–1993, introduction of low-budget mechanization associated with very high gold prices resulting in large mining area expansion and high water siltation levels; (iii) 1994–2003, general recession of ASGM activities and exhaustion of easy-access gold deposits, resulting in decreased TSS; (iv) 2004 to present, intensification of ASGM encouraged by high gold prices, resulting in an increase of TSS.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.125

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.200
Teacher spread0.188 · 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