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Record W3020847961 · doi:10.1101/2020.05.04.077818

Change in terrestrial human footprint drives continued loss of intact ecosystems

2020· preprint· en· W3020847961 on OpenAlexaff
Brooke Williams, Oscar Venter, James R. Allan, Scott Atkinson, Jose A. Rehbein, Michelle Ward, Moreno Di Marco, Hedley S. Grantham, Jamison Ervin, S. J. Goetz, Andrew J. Hansen, Patrick Jantz, Rajeev Pillay, Susana Rodríguez‐Buriticá, Christina Supples, Anne Virnig, James Watson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of Northern British Columbia
FundersAustralian GovernmentNational Aeronautics and Space Administration
KeywordsShrublandBiomeEcosystemDisturbance (geology)Terrestrial ecosystemRainforestFootprintSubtropicsGeographyWildernessEnvironmental scienceClimate changeEarth observationEcological footprintEcologyPhysical geographySustainabilityGeologySatelliteArchaeology

Abstract

fetched live from OpenAlex

Summary Our ability to map humanity’s influence across Earth has evolved, thanks to powerful computing, a network of earth observing satellites, and new bottom-up census and crowd-sourced data. Here, we provide the latest temporally inter-comparable maps of the terrestrial Human Footprint, and assessment of change in human pressure at global, biome, and ecoregional scales. In 2013, 42% of terrestrial Earth could be considered relatively free of anthropogenic disturbance, and 25% could be classed as ‘wilderness’ (the least degraded end of the human footprint spectrum). Between 2000 and 2013, 1.9 million km 2 - an area the size of Mexico - of land relatively free of human disturbance became highly modified. The majority of this occurred within tropical and subtropical grasslands, savannah, and shrubland ecosystems, but the rainforests of Southeast Asia also underwent rapid modification. Our results show that humanity’s footprint is eroding Earth’s last intact ecosystems, and greater efforts are urgently needed to retain them.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.041
GPT teacher head0.290
Teacher spread0.249 · 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.

Study designObservational
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

Citations40
Published2020
Admission routes1
Has abstractyes

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