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Harvesting the Biosphere: The Human Impact

2011· article· en· W2071040712 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.

Bibliographic record

VenuePopulation and Development Review · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiosphereEnvironmental scienceDeforestation (computer science)PhotosynthesisProductivityTerrestrial ecosystemPrimary productionWetlandNatural resource economicsAgroforestryPopulationEcologyEcosystemEnvironmental protectionBiologyEconomicsBotany

Abstract

fetched live from OpenAlex

The human species has evolved to dominate the biosphere: global anthropomass is now an order of magnitude greater than the mass of all wild terrestrial mammals. As a result, our dependence on harvesting the products of photosynthesis for food, animal feed, raw materials, and energy has grown to make substantial global impacts. During the past two millennia these harvests, and changes of land use due to deforestation and conversions of grasslands and wetlands, have reduced the stock of global terrestrial plant mass by as much as 45 percent, with the twentieth-century reduction amounting to more than 15 percent. Current annual harvests of phytomass have been a significant share of the global net primary productivity (NPP, the total amount of new plant tissues created by photosynthesis). Some studies put the human appropriation of NPP (the ratio of these two variables) as high as 40 percent but the measure itself is problematic. Future population growth and improved quality of life will result in additional claims on the biosphere, but options to accommodate these demands exist without severely compromising the irreplaceable biospheric services.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.998

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)0.0030.000

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 head0.040
GPT teacher head0.274
Teacher spread0.234 · 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