Some human, aircraft and animal factors affecting aerial surveys: how to enumerate animals from the air
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.
Bibliographic record
Abstract
Aerial surveys of wildlife involve a noisy platform carrying one or more observers moving over animals in order to quantify their abundance. This simple-sounding system encapsulates limits to human visual acuity and human concentration, visual attention, salience of target objects within the viewed scene, characteristics of survey platforms and facets of animal behaviours that affect the detection of animals by the airborne observers. These facets are too often ignored in aerial surveys, yet are inherent sources of counting error. Here we briefly review factors limiting the ability of observers to detect animals from aerial platforms in a range of sites, including characteristics of the aircraft, observers and target animals. Some of the previously uninvestigated limitations identified in the review were studied in central and western New South Wales, showing that inaccuracies of human memory and enumeration processes are sources of bias in aerial survey estimates. Standard protocols that minimise or account for the reviewed factors in aerial surveys of wildlife are recommended.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it