Peer Review #2 of "Reducing publication delay to improve the efficiency and impact of conservation science (v0.2)"
Bibliographic record
Abstract
Evidence-based decision-making is most effective with comprehensive access to scientific studies.If studies face significant publication delays or barriers, the useful information they contain may not reach decision-makers in a timely manner.This represents a potential problem for mission-oriented disciplines where access to the latest data is required to ensure effective actions are undertaken.We sought to analyse the severity of publication delay in conservation science -a field that requires urgent action to prevent the loss of biodiversity.We used the Conservation Evidence database to assess the length of publication delay (time from finishing data collection to publication) in the literature that tests the effectiveness of conservation interventions.From 7,447 peer-reviewed and nonpeer-reviewed studies of conservation interventions published over eleven decades, we find that the raw mean publication delay was 3.2 years (±2SD = 0.1) and varied by conservation subject.A significantly shorter delay was observed for studies focused on Bee Conservation, Sustainable Aquaculture, Management of Captive Animals, Amphibian Conservation, and Control of Freshwater Invasive Species (Estimated Marginal Mean range from 1.4-1.9years).Publication delay was significantly shorter for the non-peer-reviewed literature (Estimated Marginal Mean delay of 1.9 years ±0.2) compared to the peerreviewed literature (i.e., scientific journals; Estimated Marginal Mean delay of 3.0 years ± 0.1).We found publication delay has significantly increased over time (an increase of ~1.2 years from 1912 (1.4 years ±0.2) to 2020 (2.6 years ±0.1)), but this change was much PeerJ reviewing PDF | (2021:05
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.120 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.285 | 0.176 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".