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Record W3026633123 · doi:10.1111/conl.12719

Dams and protected areas: Quantifying the spatial and temporal extent of global dam construction within protected areas

2020· article· en· W3026633123 on OpenAlex
Michele Thieme, D. S. Khrystenko, Siyu Qin, Rachel Golden Kroner, Bernhard Lehner, Shalynn M. Pack, Klement Tockner, Christiane Zarfl, Natalie Shahbol, Michael B. Mascia

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

VenueConservation Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill University
FundersWorld Wildlife FundConservation InternationalU.S. Department of State
KeywordsHydropowerBiodiversityEnvironmental scienceEcosystemEnvironmental resource managementGeographyWater resource managementEnvironmental protectionEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Protected areas (PAs) are an essential tool for freshwater biodiversity conservation. Given past and expected future global increases in dams and impacts of dams on freshwater ecosystems, we document the number of dams existing or planned within PAs, their history, and the extent of PA downgrading, downsizing, and degazettement (PADDD) proximally caused by dams. Globally, at least 1,249 large dams are located within PAs; two‐thirds (907) were built before PA establishment. Additionally, 14% of planned geolocated hydropower dams (509 dams) are located within PAs. PADDD events have also legalized dam construction within existing PAs. Environmental safeguards should preclude development of dams within or adjacent to PAs and prioritize dams within PAs for possible removal and restoration.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.303

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 studies0.0000.001
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 head0.024
GPT teacher head0.230
Teacher spread0.206 · 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