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Record W4206684981 · doi:10.22215/etd/2021-14781

A Test of the Space-For-Time Substitution Hypothesis: North American Bird Responses to Forest Loss over Space do not Predict Their Responses Over Time

2021· dissertation· en· W4206684981 on OpenAlexaffabout
Kayla Attinello

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsCarleton University
Fundersnot available
KeywordsBreeding bird surveyEcologySpecies richnessAbundance (ecology)GeographyHabitatForest coverSpace (punctuation)Substitution (logic)Bird conservationBiologyComputer science

Abstract

fetched live from OpenAlex

The practice of space-for-time substitution in landscape ecology has provided vital insights for conservation policy, but whether these insights are reliable remains inconclusive.Here, my objective was to test space-for-time substitution using data from the North American Breeding Bird Survey (BBS) and Global Forest Change (GFC) to compare the effects of landscape-level forest cover on bird community metrics over time and space across 31 space-time comparisons in the United States and Canada.Temporal and spatial effects of forest cover on mean bird species richness and mean bird abundance were weakly correlated across the 31 comparisons for both forest and open-habitat species.Bird-forest cover relationships measured over time were more variable and inconsistent than those observed in space.Overall, my study results do not support the use of space-for-time substitution when studying North American birds.

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)0.024
metaresearch head score (Gemma)0.081
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 score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.081
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.002
Science and technology studies0.0010.006
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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