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Record W4405920471 · doi:10.1038/s43247-024-01963-x

Rocket launches threaten global biodiversity conservation

2024· article· en· W4405920471 on OpenAlexaff
Lee Ping Ang, Fanhao Kong, Enrique Hernández-Rodríguez, Qiuyu Liu, Carlos Cerrejόn, Mariano J. Feldman, Lei Shu, L. Ye, Lei Gao, Lee Ling Ang, Xiangbo Yin

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsUniversité LavalUniversité du Québec en Abitibi-Témiscamingue
FundersChina Postdoctoral Science FoundationPhilipps-Universität Marburg
KeywordsBiodiversityBiodiversity conservationRocket (weapon)Environmental scienceEnvironmental planningEnvironmental protectionEnvironmental resource managementAeronauticsEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Satellite technologies are essential for global conservation actions through providing continuous, real-time Earth monitoring. However, development of these technologies necessitates an increase in rocket launches, which introduces new threats to biodiversity. Here, we mapped rocket launch sites and assessed their threats on protected areas and terrestrial biodiversity. Our analysis revealed that over 90% launch sites are within areas where unprotected habitats excesses 50% and over 62% of operating sites are located within or near protected areas. The threats from rocket launches are potentially associated with biomes, coordinates, and proximity to oceans. In particular, threatened terrestrial species in Tropical and Subtropical Moist Broadleaf Forests are more vulnerable to these risks compared to species in other biomes. Without strategic planning, the continued growth of rocket launches could create conflicts between technological development and conservation efforts, undermining the achievement of UN Biodiversity Goals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.067
GPT teacher head0.283
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2024
Admission routes1
Has abstractyes

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